P5¶
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import pandas as pd
import numpy as np
import pickle
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"
import warnings
warnings.filterwarnings('ignore')
import pandas as pd
import numpy as np
import pickle
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"
import warnings
warnings.filterwarnings('ignore')
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docs = pd.read_csv("QueryResults.csv")
docs = pd.read_csv("QueryResults.csv")
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docs.set_index('Id').sort_index()
docs.set_index('Id').sort_index()
Out[3]:
| Title | Body | Tags | Score | ViewCount | FavoriteCount | AnswerCount | |
|---|---|---|---|---|---|---|---|
| Id | |||||||
| 4 | How to convert a Decimal to a Double in C#? | <p>I want to use a <code>Track-Bar</code> to c... | <c#><floating-point><type-conversion><double><... | 759 | 64063 | 58 | 12 |
| 11 | Calculate relative time in C# | <p>Given a specific <code>DateTime</code> valu... | <c#><datetime><time><datediff><relative-time-s... | 1612 | 188799 | 552 | 41 |
| 13 | Determine a user's timezone | <p>Is there a standard way for a web server to... | <html><browser><timezone><user-agent><timezone... | 673 | 248432 | 157 | 27 |
| 19 | What is the fastest way to get the value of π? | <p>I'm looking for the fastest way to obtain t... | <performance><algorithm><language-agnostic><un... | 341 | 63943 | 86 | 23 |
| 88 | Is gettimeofday() guaranteed to be of microsec... | <p>I am porting a game, that was originally wr... | <linux><winapi><visual-c++><unix><timer> | 104 | 43767 | 19 | 10 |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 69729326 | Endless sine generation in C | <p>I am working on a project which incorporate... | <c><performance><time><precision><trigonometry> | 91 | 10074 | 25 | 12 |
| 69832748 | Error "Error: A <Route> is only ever to be use... | <p>I am trying to use routing for the first ti... | <javascript><node.js><reactjs><frameworks><rea... | 57 | 77406 | 13 | 14 |
| 69875125 | find_element_by_* commands are deprecated in s... | <p>When starting the function</p>\n<pre><code>... | <python><selenium><selenium-webdriver><webdriv... | 52 | 59346 | 25 | 3 |
| 70358643 | "You are running create-react-app 4.0.3 which ... | <p>I got an error while creating a React appli... | <javascript><reactjs><npm-install><yarnpkg><npx> | 167 | 76256 | 35 | 6 |
| 70926799 | CentOS through a VM - no URLs in mirrorlist | <p>I am trying to run a <a href="https://en.wi... | <linux><centos><vagrant><virtualbox><redhat> | 49 | 48154 | 20 | 2 |
27128 rows × 7 columns
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docs.drop(['Id', 'Score', 'ViewCount', 'FavoriteCount','AnswerCount'], axis=1, inplace=True)
docs.drop(['Id', 'Score', 'ViewCount', 'FavoriteCount','AnswerCount'], axis=1, inplace=True)
Data Preparation¶
Tags preprocessing¶
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# tokenize the tags
def tokenizeTags(tags):
"""
Remove some unwanted characters
"""
tags = tags.replace('<', ' ')
tags = tags.replace('>', '')
return tags.strip().split(' ')
docs['Tags'] = docs['Tags'].apply(tokenizeTags)
# tokenize the tags
def tokenizeTags(tags):
"""
Remove some unwanted characters
"""
tags = tags.replace('<', ' ')
tags = tags.replace('>', '')
return tags.strip().split(' ')
docs['Tags'] = docs['Tags'].apply(tokenizeTags)
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# one doc tags sample
docs['Tags'][0]
# one doc tags sample
docs['Tags'][0]
Out[6]:
['javascript', 'geolocation', 'projection', 'processing.js', 'proj4js']
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# get 200 most frequent tags
def getMostFrequentTags(df: pd.DataFrame, col: str, max_tags_count = 200) -> pd.DataFrame:
all_tags = [item for sublist in df[col] for item in sublist]
unique, counts = np.unique(all_tags, return_counts=True)
return pd.DataFrame({'word': unique, 'count':counts}).sort_values('count', ascending=False)[:max_tags_count]
common_tags = getMostFrequentTags(docs, 'Tags').word.values
# get 200 most frequent tags
def getMostFrequentTags(df: pd.DataFrame, col: str, max_tags_count = 200) -> pd.DataFrame:
all_tags = [item for sublist in df[col] for item in sublist]
unique, counts = np.unique(all_tags, return_counts=True)
return pd.DataFrame({'word': unique, 'count':counts}).sort_values('count', ascending=False)[:max_tags_count]
common_tags = getMostFrequentTags(docs, 'Tags').word.values
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# filter tags of each document to restrict to most common
def get_most_tag(row):
return [tag for tag in row if tag in common_tags]
docs['tags'] = docs['Tags'].apply(get_most_tag)
# filter tags of each document to restrict to most common
def get_most_tag(row):
return [tag for tag in row if tag in common_tags]
docs['tags'] = docs['Tags'].apply(get_most_tag)
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docs.drop('Tags', axis=1, inplace=True)
docs.drop('Tags', axis=1, inplace=True)
Word Cloud¶
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# TODO WIP
# from wordcloud import WordCloud
#
# words = str([tag for row in docs.tags for tag in row])
# # Create a WordCloud object
# wordcloud = WordCloud(background_color="white", max_words=200, contour_width=3, contour_color='steelblue')
# # Generate a word cloud
# wordcloud.generate(words)
# # Visualize the word cloud
# wordcloud.to_image()
# TODO WIP
# from wordcloud import WordCloud
#
# words = str([tag for row in docs.tags for tag in row])
# # Create a WordCloud object
# wordcloud = WordCloud(background_color="white", max_words=200, contour_width=3, contour_color='steelblue')
# # Generate a word cloud
# wordcloud.generate(words)
# # Visualize the word cloud
# wordcloud.to_image()
Words preprocessing¶
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# add title into the body
docs['body'] = docs['Title'] + '\n' + docs['Body']
# add title into the body
docs['body'] = docs['Title'] + '\n' + docs['Body']
Remove code snippets¶
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from bs4 import BeautifulSoup
def remove_code_snippets(docs):
"""
Remove code snippets from docs
surrounded by <pre> tags
"""
# ensure '<' & '>' chars are set, not the unicode char
docs = docs.apply(lambda d: d.replace('<', '<').replace('>', '>'))
ret = []
for doc in docs:
soup = BeautifulSoup(doc, 'html.parser')
removals = soup.find_all('pre')
for pre in removals:
pre.decompose()
ret.append(soup.get_text())
return ret
from bs4 import BeautifulSoup
def remove_code_snippets(docs):
"""
Remove code snippets from docs
surrounded by
tags
"""
# ensure '<' & '>' chars are set, not the unicode char
docs = docs.apply(lambda d: d.replace('<', '<').replace('>', '>'))
ret = []
for doc in docs:
soup = BeautifulSoup(doc, 'html.parser')
removals = soup.find_all('pre')
for pre in removals:
pre.decompose()
ret.append(soup.get_text())
return ret
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docs.body = remove_code_snippets(docs.body)
docs.body = remove_code_snippets(docs.body)
Remove Stop words, Lemmatize, Stemmize, Tokenize¶
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# remove stop words
# lemmatize (convert and remove prefixes and suffixes to ignore the gender, plural, verb : walking, walks, walked, walk -> walk)
# and/or stemmize (take the same root of words : universal / university -> univers)
import re
import string
import nltk
from nltk.corpus import stopwords
from nltk.stem.snowball import EnglishStemmer
from nltk.stem import WordNetLemmatizer
stopwords = set(stopwords.words('english'))
filtre_stopw = lambda text: [token.lower() for token in text if token.lower() not in stopwords]
# replace words with stem
def tokenize_sw_stem(doc):
tokenizer = nltk.RegexpTokenizer(r'\w+')
stemmer = EnglishStemmer()
filtered = filtre_stopw(tokenizer.tokenize(doc))
ret = " ".join([stemmer.stem(s) for s in filtered] )
return ret
# replace words with lem
def tokenize_sw_lem(doc):
lemmatizer = WordNetLemmatizer()
filtered = filtre_stopw(nltk.tokenize.word_tokenize(doc))
# filtered = filtre_stopw(tokenizer.tokenize(doc))
ret = " ".join([lemmatizer.lemmatize(s) for s in filtered] )
# TODO manage .net token
# manage c# token
ret = re.sub(r'c #', 'c#', ret)
# temp
# ret = re.sub(r' #', '', ret)
# ret = re.sub(r'# ', '', ret)
# removes punctuation, comes from string.punctuation, without some special characters present into the tags (c#, c++, objective-c, )
ret = ret.translate(str.maketrans('', '', r"""!"$%&'()*,/:;<=>?@[\]^_`{|}~"""))
# removes all non-word
ret = re.sub(' \W+ ',' ', ret)
# remove single char, except 'c', 'r'
# ret = re.sub(r'\s[abd-qs-z]{1}\s', ' ', ret)
ret = re.sub(r'\s[abd-q]{1}\s', ' ', ret)
ret = re.sub(r'\s[s-z]{1}\s', ' ', ret)
ret = re.sub(r'\s\+{1}', ' ', ret)
# ret = re.sub(r'\s[abd-qs-z]{1}\s', ' ', ret)
# ret = re.sub(r'\s[s-z]{1}', ' ', ret)
# remove double or more spaces
ret = re.sub(r' {2,}', ' ', ret)
return ret
# words_stem = list(map(tokenize_sw_stem, docs.body))
# words_lem = list(map(tokenize_sw_lem, docs.body))
# test
test_string = "this is a test string. Contains with __ chars and c++, C# tags, java, .net, r language, c lang , ; and some isolated letters a b c d e z x end signs + "
tokenize_sw_lem(test_string)
print('C#')
index = 3632
docs.body[index]
tokenize_sw_lem(docs.body[index])
print('c++')
index = 70
docs.body[index]
tokenize_sw_lem(docs.body[index])
print('.net')
index = 1460
docs.body[index]
tokenize_sw_lem(docs.body[index])
print('MISC')
index = 203
docs.body[index]
tokenize_sw_lem(docs.body[index])
# remove stop words
# lemmatize (convert and remove prefixes and suffixes to ignore the gender, plural, verb : walking, walks, walked, walk -> walk)
# and/or stemmize (take the same root of words : universal / university -> univers)
import re
import string
import nltk
from nltk.corpus import stopwords
from nltk.stem.snowball import EnglishStemmer
from nltk.stem import WordNetLemmatizer
stopwords = set(stopwords.words('english'))
filtre_stopw = lambda text: [token.lower() for token in text if token.lower() not in stopwords]
# replace words with stem
def tokenize_sw_stem(doc):
tokenizer = nltk.RegexpTokenizer(r'\w+')
stemmer = EnglishStemmer()
filtered = filtre_stopw(tokenizer.tokenize(doc))
ret = " ".join([stemmer.stem(s) for s in filtered] )
return ret
# replace words with lem
def tokenize_sw_lem(doc):
lemmatizer = WordNetLemmatizer()
filtered = filtre_stopw(nltk.tokenize.word_tokenize(doc))
# filtered = filtre_stopw(tokenizer.tokenize(doc))
ret = " ".join([lemmatizer.lemmatize(s) for s in filtered] )
# TODO manage .net token
# manage c# token
ret = re.sub(r'c #', 'c#', ret)
# temp
# ret = re.sub(r' #', '', ret)
# ret = re.sub(r'# ', '', ret)
# removes punctuation, comes from string.punctuation, without some special characters present into the tags (c#, c++, objective-c, )
ret = ret.translate(str.maketrans('', '', r"""!"$%&'()*,/:;<=>?@[\]^_`{|}~"""))
# removes all non-word
ret = re.sub(' \W+ ',' ', ret)
# remove single char, except 'c', 'r'
# ret = re.sub(r'\s[abd-qs-z]{1}\s', ' ', ret)
ret = re.sub(r'\s[abd-q]{1}\s', ' ', ret)
ret = re.sub(r'\s[s-z]{1}\s', ' ', ret)
ret = re.sub(r'\s\+{1}', ' ', ret)
# ret = re.sub(r'\s[abd-qs-z]{1}\s', ' ', ret)
# ret = re.sub(r'\s[s-z]{1}', ' ', ret)
# remove double or more spaces
ret = re.sub(r' {2,}', ' ', ret)
return ret
# words_stem = list(map(tokenize_sw_stem, docs.body))
# words_lem = list(map(tokenize_sw_lem, docs.body))
# test
test_string = "this is a test string. Contains with __ chars and c++, C# tags, java, .net, r language, c lang , ; and some isolated letters a b c d e z x end signs + "
tokenize_sw_lem(test_string)
print('C#')
index = 3632
docs.body[index]
tokenize_sw_lem(docs.body[index])
print('c++')
index = 70
docs.body[index]
tokenize_sw_lem(docs.body[index])
print('.net')
index = 1460
docs.body[index]
tokenize_sw_lem(docs.body[index])
print('MISC')
index = 203
docs.body[index]
tokenize_sw_lem(docs.body[index])
Out[14]:
'test string contains char c++ c# tag java .net r language c lang isolated letter c x end sign '
C#
Out[14]:
"c# - WebRequest HTTP POST with Cookie (port from curl script)\nThe IBM RTC RESTful api gives an example of a shell script for authenticating with the server:\n\nThis works perfectly, however i need to authenticate with the server using c#.\nSo far i have the following, but it isn't working (returns the authorization failed page):\n\n"
Out[14]:
'c# webrequest http post cookie port curl script ibm rtc restful api give example shell script authenticating server work perfectly however need authenticate server using c# far following nt working return authorization failed page '
c++
Out[14]:
'Test Automation with Embedded Hardware\nHas anyone had success automating testing directly on embedded hardware?\nSpecifically, I am thinking of automating a battery of unit tests for hardware layer modules. We need to have greater confidence in our hardware layer code. A lot of our projects use interrupt driven timers, ADCs, serial io, serial SPI devices (flash memory) etc..\nIs this even worth the effort?\nWe typically target:\nProcessor: 8 or 16 bit microcontrollers (some DSP stuff)\nLanguage: C (sometimes c++). \n'
Out[14]:
'test automation embedded hardware anyone success automating testing directly embedded hardware specifically thinking automating battery unit test hardware layer module need greater confidence hardware layer code lot project use interrupt driven timer adcs serial io serial spi device flash memory etc even worth effort typically target processor 8 16 bit microcontrollers dsp stuff language c sometimes c++ .'
.net
Out[14]:
".NET Web API 2 OWIN Bearer Token Authentication\nI'm implementing a Web API 2 service architecture in my .NET web application. The client consuming the requests is pure javascript, no mvc/asp.net. I'm using OWIN to try to enable token authentication per this article OWIN Bearer Token Authentication with Web API Sample. I seem to be missing something with the authentication step after its authorized.\nMy login looks like:\n\nIt returns\n\nThen I try to set the HTTP header Bearer on further requests in AngularJS like:\n\nto a API like:\n\nbut no matter what I do this service is 'unauthorized'. Am I missing something here?\n"
Out[14]:
'.net web api 2 owin bearer token authentication implementing web api 2 service architecture .net web application client consuming request pure javascript mvcasp.net using owin try enable token authentication per article owin bearer token authentication web api sample seem missing something authentication step authorized login look like return try set http header bearer request angularjs like api like matter service unauthorized missing something '
MISC
Out[14]:
"Hosting the .NET runtime in a Delphi Program\nI was looking into using some .NET code from within a Delphi program, I will need to make my program extensible using .net assemblies and predefined functions (I already support regular DLLs).\nAfter a lot of searching online, I found Managed-VCL, but I'm not ready to pay $250 for what I need, I also found some newsgroups with code that's incomplete and doesn't work.\nI'm using Delphi 2007 for win32. What can I use to dynamically execute a function from an assembly with predefined parameters?\nSomething like:\n\nI just want to add that I need to be able to load an arbitrary assemblies (maybe all the assemblies in a specific folder), so creating a C# wrapper may not work.\n"
Out[14]:
'hosting .net runtime delphi program looking using .net code within delphi program need make program extensible using .net assembly predefined function already support regular dlls lot searching online found managed-vcl ready pay 250 need also found newsgroups code incomplete nt work using delphi 2007 win32 use dynamically execute function assembly predefined parameter something like want add need able load arbitrary assembly maybe assembly specific folder creating c# wrapper may work .'
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# let's choose the lemmatize version of the corpus, since it results in real words that could be used as tags
docs['tokenized_body'] = list(map(tokenize_sw_lem, docs.body))
# let's choose the lemmatize version of the corpus, since it results in real words that could be used as tags
docs['tokenized_body'] = list(map(tokenize_sw_lem, docs.body))
Remove remaining 'bad words'¶
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import re
def remove_words_old(words):
ret = []
for word in words:
# print(f'\nprocessing sentence: {word}\n')
flat = re.sub(r'[0-9]+', '', word) # words that are numbers
flat = re.sub(r'_+', '', flat) # words that contain underscores
flat = re.sub(r'-+', '', flat) # words that contain dashes
flat = re.sub(r'\+', '', flat)
flat = re.sub(r'\w{15,}', '', flat) # words longer than 15 chars are most probably code (i.e. myClass.myMethodName())
flat = " ".join(flat.split()) # remove double spaces
# print(f'\ncurrated : {flat}')
ret.append(flat)
return ret
def remove_words(words):
ret = []
# print(f'\nprocessing sentence: {word}\n')
flat = re.sub(r'[0-9]+', '', words) # words that are numbers
flat = re.sub(r'_+', '', flat) # words that contain underscores
flat = re.sub(r'-+', '', flat) # words that contain dashes
# flat = re.sub(r'\+', '', flat)
flat = re.sub(r'\w{15,}', '', flat) # words longer than 15 chars are most probably code (i.e. myClass.myMethodName())
flat = " ".join(flat.split()) # remove double spaces
# print(f'\ncurrated : {flat}')
ret.append(flat)
return flat
print('MISC')
index = 203
docs.body[index]
remove_words(docs.body[index])
import re
def remove_words_old(words):
ret = []
for word in words:
# print(f'\nprocessing sentence: {word}\n')
flat = re.sub(r'[0-9]+', '', word) # words that are numbers
flat = re.sub(r'_+', '', flat) # words that contain underscores
flat = re.sub(r'-+', '', flat) # words that contain dashes
flat = re.sub(r'\+', '', flat)
flat = re.sub(r'\w{15,}', '', flat) # words longer than 15 chars are most probably code (i.e. myClass.myMethodName())
flat = " ".join(flat.split()) # remove double spaces
# print(f'\ncurrated : {flat}')
ret.append(flat)
return ret
def remove_words(words):
ret = []
# print(f'\nprocessing sentence: {word}\n')
flat = re.sub(r'[0-9]+', '', words) # words that are numbers
flat = re.sub(r'_+', '', flat) # words that contain underscores
flat = re.sub(r'-+', '', flat) # words that contain dashes
# flat = re.sub(r'\+', '', flat)
flat = re.sub(r'\w{15,}', '', flat) # words longer than 15 chars are most probably code (i.e. myClass.myMethodName())
flat = " ".join(flat.split()) # remove double spaces
# print(f'\ncurrated : {flat}')
ret.append(flat)
return flat
print('MISC')
index = 203
docs.body[index]
remove_words(docs.body[index])
MISC
Out[16]:
"Hosting the .NET runtime in a Delphi Program\nI was looking into using some .NET code from within a Delphi program, I will need to make my program extensible using .net assemblies and predefined functions (I already support regular DLLs).\nAfter a lot of searching online, I found Managed-VCL, but I'm not ready to pay $250 for what I need, I also found some newsgroups with code that's incomplete and doesn't work.\nI'm using Delphi 2007 for win32. What can I use to dynamically execute a function from an assembly with predefined parameters?\nSomething like:\n\nI just want to add that I need to be able to load an arbitrary assemblies (maybe all the assemblies in a specific folder), so creating a C# wrapper may not work.\n"
Out[16]:
"Hosting the .NET runtime in a Delphi Program I was looking into using some .NET code from within a Delphi program, I will need to make my program extensible using .net assemblies and predefined functions (I already support regular DLLs). After a lot of searching online, I found ManagedVCL, but I'm not ready to pay $ for what I need, I also found some newsgroups with code that's incomplete and doesn't work. I'm using Delphi for win. What can I use to dynamically execute a function from an assembly with predefined parameters? Something like: I just want to add that I need to be able to load an arbitrary assemblies (maybe all the assemblies in a specific folder), so creating a C# wrapper may not work."
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docs['tokenized_body_cleaned'] = docs.tokenized_body.apply(remove_words)
docs['tokenized_body_cleaned'] = docs.tokenized_body.apply(remove_words)
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index = 28
docs.body[index]
docs.tokenized_body[index]
docs.tokenized_body_cleaned[index]
index = 28
docs.body[index]
docs.tokenized_body[index]
docs.tokenized_body_cleaned[index]
Out[18]:
'How to mock window.location.href with Jest + Vuejs?\nCurrently, I am implementing unit test for my project and there is a file that contained window.location.href.\nI want to mock this to test and here is my sample code:\n\nBut I get this error:\n\nI had tried some solutions but not resolve it. I need some hints to help me get out of this trouble. Plz help.\n'
Out[18]:
'mock window.location.href jest vuejs currently implementing unit test project file contained window.location.href want mock test sample code get error tried solution resolve need hint help get trouble plz help .'
Out[18]:
'mock window.location.href jest vuejs currently implementing unit test project file contained window.location.href want mock test sample code get error tried solution resolve need hint help get trouble plz help .'
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docs[docs.body.str.contains('...')]
docs[docs.body.str.contains('...')]
Out[19]:
| Title | Body | tags | body | tokenized_body | tokenized_body_cleaned | |
|---|---|---|---|---|---|---|
| 0 | Mercator longitude and latitude calculations t... | <p>I have this image. It's a map of the UK (no... | [javascript] | Mercator longitude and latitude calculations t... | mercator longitude latitude calculation croppe... | mercator longitude latitude calculation croppe... |
| 1 | Starting point for learning CAD/CAE file formats? | <p>We are developing some stress and strain an... | [c++] | Starting point for learning CAD/CAE file forma... | starting point learning cadcae file format dev... | starting point learning cadcae file format dev... |
| 2 | LINQ query on a DataTable | <p>I'm trying to perform a LINQ query on a Dat... | [c#, .net, linq] | LINQ query on a DataTable\nI'm trying to perfo... | linq query datatable trying perform linq query... | linq query datatable trying perform linq query... |
| 3 | How to overload std::swap() | <p><code>std::swap()</code> is used by many st... | [c++, performance, optimization, stl] | How to overload std::swap()\nstd::swap() is us... | overload std swap std swap used many std conta... | overload std swap std swap used many std conta... |
| 4 | How do you get a directory listing in C? | <p>How do you scan a directory for folders and... | [c, file] | How do you get a directory listing in C?\nHow ... | get directory listing c scan directory folder ... | get directory listing c scan directory folder ... |
| ... | ... | ... | ... | ... | ... | ... |
| 27123 | Can't perform a React state update on an unmou... | <h2>Problem</h2>\n\n<p>I am writing an applica... | [javascript, reactjs, typescript] | Can't perform a React state update on an unmou... | ca nt perform react state update unmounted com... | ca nt perform react state update unmounted com... |
| 27124 | Can WPF applications be run in Linux or Mac wi... | <p>Microsoft announced .NET Core 3 comes with ... | [linux, wpf] | Can WPF applications be run in Linux or Mac wi... | wpf application run linux mac .net core 3 micr... | wpf application run linux mac .net core micros... |
| 27125 | Instance of 'SQLAlchemy' has no 'Column' membe... | <p>I'm currently trying to implement steam log... | [python, python-3.x] | Instance of 'SQLAlchemy' has no 'Column' membe... | instance sqlalchemy column member no-member cu... | instance sqlalchemy column member nomember cur... |
| 27126 | Why does `if None.__eq__("a")` seem to evaluat... | <p>If you execute the following statement in P... | [python, python-3.x, string] | Why does `if None.__eq__("a")` seem to evaluat... | none.eq seem evaluate true quite execute foll... | none.eq seem evaluate true quite execute follo... |
| 27127 | How does the "number of workers" parameter in ... | <ol>\n<li>If <code>num_workers</code> is 2, Do... | [python, memory-management, deep-learning] | How does the "number of workers" parameter in ... | number worker parameter pytorch dataloader ac... | number worker parameter pytorch dataloader act... |
27128 rows × 6 columns
Corpus size and Vocabulary¶
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# count the total number of words in the overall corpus
# ideally would to be limited to 2000 / 3000 words maximum
def get_common_vocabulary():
lem = map(lambda x : x.split(), docs.tokenized_body_cleaned)
flat_list = [word for sublist in list(lem) for word in sublist ]
# len(flat_list)
unique, counts = np.unique(flat_list, return_counts=True)
counted_words = pd.DataFrame({'word': unique, 'count': counts}).sort_index(ascending=False)
min_counts = 1000
# take the 3000 most used words in the corpus for our vocabulary
vocabulary = pd.DataFrame({'count': counts, 'word': unique}).sort_values('count', ascending=False)[:min_counts].word.values
# len(vocabulary)
# add to vocabulary the list of common tags
return np.unique(np.append(vocabulary, common_tags))
common_words = get_common_vocabulary()
common_words = ' '.join(common_words)
common_words = re.sub(r'\.{3}', '', str(common_words))
common_words = re.sub(r'\.{2}', '', common_words)
common_words = re.sub(r'\. ', '', common_words)
common_words = re.sub(r'[ ]{2,}', '', common_words)
common_words = np.array(common_words.split(' '))
common_words
# count the total number of words in the overall corpus
# ideally would to be limited to 2000 / 3000 words maximum
def get_common_vocabulary():
lem = map(lambda x : x.split(), docs.tokenized_body_cleaned)
flat_list = [word for sublist in list(lem) for word in sublist ]
# len(flat_list)
unique, counts = np.unique(flat_list, return_counts=True)
counted_words = pd.DataFrame({'word': unique, 'count': counts}).sort_index(ascending=False)
min_counts = 1000
# take the 3000 most used words in the corpus for our vocabulary
vocabulary = pd.DataFrame({'count': counts, 'word': unique}).sort_values('count', ascending=False)[:min_counts].word.values
# len(vocabulary)
# add to vocabulary the list of common tags
return np.unique(np.append(vocabulary, common_tags))
common_words = get_common_vocabulary()
common_words = ' '.join(common_words)
common_words = re.sub(r'\.{3}', '', str(common_words))
common_words = re.sub(r'\.{2}', '', common_words)
common_words = re.sub(r'\. ', '', common_words)
common_words = re.sub(r'[ ]{2,}', '', common_words)
common_words = np.array(common_words.split(' '))
common_words
Out[20]:
array(['.net', 'able', 'access', ..., 'year', 'yes', 'yet'], dtype='<U22')
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# reduce our corpus to the common vocabulary
def reduce_body(body):
ret = []
for word in body.split(' '):
# if word in np.append(common_words, common_tags):
if word in common_words:
ret.append(word)
return ' '.join(ret)
# test
# reduce_body(docs.tokenized_body[1])
docs['reduced_body'] = docs.tokenized_body_cleaned.apply(reduce_body)
# reduce our corpus to the common vocabulary
def reduce_body(body):
ret = []
for word in body.split(' '):
# if word in np.append(common_words, common_tags):
if word in common_words:
ret.append(word)
return ' '.join(ret)
# test
# reduce_body(docs.tokenized_body[1])
docs['reduced_body'] = docs.tokenized_body_cleaned.apply(reduce_body)
MultiLabel Binarizer¶
Tags¶
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from sklearn.preprocessing import MultiLabelBinarizer
def mlb_tags():
mlb_t = MultiLabelBinarizer()
# fit the mlb with common tags (200)
mlb_t.fit([common_tags])
print(mlb_t.classes_.shape)
docs['mlb_tags'] = mlb_t.transform(docs.tags).tolist()
return mlb_t
def mlb_body():
mlb_b = MultiLabelBinarizer()
mlb_b.fit([common_words])
print(mlb_b.classes_.shape)
docs['mlb_reduced_body'] = mlb_b.transform(docs.reduced_body).tolist()
return mlb_b
from sklearn.preprocessing import MultiLabelBinarizer
def mlb_tags():
mlb_t = MultiLabelBinarizer()
# fit the mlb with common tags (200)
mlb_t.fit([common_tags])
print(mlb_t.classes_.shape)
docs['mlb_tags'] = mlb_t.transform(docs.tags).tolist()
return mlb_t
def mlb_body():
mlb_b = MultiLabelBinarizer()
mlb_b.fit([common_words])
print(mlb_b.classes_.shape)
docs['mlb_reduced_body'] = mlb_b.transform(docs.reduced_body).tolist()
return mlb_b
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mlb_t = mlb_tags()
mlb_t = mlb_tags()
(200,)
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mlb_b = mlb_body()
mlb_b = mlb_body()
(1102,)
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# test mlb on body
# c = mlb_b.classes_
# np.sort(docs.reduced_body[0].split())
test_str = 'java'
# df = pd.DataFrame(mlb_b.transform([[test_str]]).T)
# index = df[df[0] == 1].index.values[0]
# assert mlb_b.classes_[index] == test_str
mlb_b.inverse_transform(mlb_b.transform([[test_str]]))
# test mlb on body
# c = mlb_b.classes_
# np.sort(docs.reduced_body[0].split())
test_str = 'java'
# df = pd.DataFrame(mlb_b.transform([[test_str]]).T)
# index = df[df[0] == 1].index.values[0]
# assert mlb_b.classes_[index] == test_str
mlb_b.inverse_transform(mlb_b.transform([[test_str]]))
Out[25]:
[('java',)]
Data Analysis¶
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docs['no_tags'] = [len(t) for t in docs.tags]
docs['no_tags'] = [len(t) for t in docs.tags]
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# number of tags per documents
docs.no_tags.plot.hist(figsize=(10, 8))
# most of the documents have 2 or 3 tags
# number of tags per documents
docs.no_tags.plot.hist(figsize=(10, 8))
# most of the documents have 2 or 3 tags
Out[27]:
<AxesSubplot:ylabel='Frequency'>
Train, Test Sets¶
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from sklearn.model_selection import train_test_split
# X = docs.reduced_body
# y = docs.mlb_tags
# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)
from sklearn.model_selection import train_test_split
# X = docs.reduced_body
# y = docs.mlb_tags
# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)
Bag Of Words / Tf-Idf¶
Bag of words uses CountVectorizer, then we look for the most frequent word to suggest a tag
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from sklearn.feature_extraction.text import CountVectorizer
cv = CountVectorizer(vocabulary=common_words)
bow = cv.fit_transform(docs.reduced_body)
data_dense = bow.todense()
print("Sparsity: ", ((data_dense > 0).sum()/data_dense.size)*100, "%")
from sklearn.feature_extraction.text import CountVectorizer
cv = CountVectorizer(vocabulary=common_words)
bow = cv.fit_transform(docs.reduced_body)
data_dense = bow.todense()
print("Sparsity: ", ((data_dense > 0).sum()/data_dense.size)*100, "%")
Sparsity: 2.8385362449229063 %
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bow.toarray()
cv.get_feature_names_out()
bow.toarray()
cv.get_feature_names_out()
Out[30]:
array([[0, 2, 0, ..., 0, 0, 0],
[0, 1, 0, ..., 0, 0, 0],
[0, 0, 0, ..., 0, 0, 0],
...,
[0, 0, 0, ..., 0, 0, 0],
[0, 0, 0, ..., 0, 0, 0],
[0, 0, 0, ..., 0, 0, 0]])
Out[30]:
array(['.net', 'able', 'access', ..., 'year', 'yes', 'yet'], dtype=object)
Tf-idf takes the frequency of a word from a sentence in a context of a corpus, then we take the most frequent words to suggest a tag
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from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer(vocabulary=common_words, ngram_range=(1, 2))
vectorizer.fit(docs['reduced_body'])
vectorizer.get_feature_names_out()
from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer(vocabulary=common_words, ngram_range=(1, 2))
vectorizer.fit(docs['reduced_body'])
vectorizer.get_feature_names_out()
Out[31]:
TfidfVectorizer(ngram_range=(1, 2),
vocabulary=array(['.net', 'able', 'access', ..., 'year', 'yes', 'yet'], dtype='<U22'))
Out[31]:
array(['.net', 'able', 'access', ..., 'year', 'yes', 'yet'], dtype=object)
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with open('vectorizer', 'wb') as v :
pickle.dump(vectorizer, v)
with open('vectorizer', 'wb') as v :
pickle.dump(vectorizer, v)
Prediction method for tf-idf¶
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# predicition - nouvelle methode
# TODO optimize - temps de calcul trop long ~ 3'30
def predict_tfidf(doc, filter_tags=True) -> list[str]:
"""
Returns a list of tags representative to the document provided, predicted from tf-idf
Parameters
----------
doc: iterable
a list of words to compute tf-idf
filter_tags: boolean
defines if returned tags must be part of a currated list of tags
Returns
-------
ret : a list of strings, may be empty
"""
res = vectorizer.transform([doc]).T.todense()
res = pd.DataFrame({'tfidf': res.tolist()}).sort_values('tfidf', ascending=False)[:5]
res = [ vectorizer.get_feature_names_out()[found_tag_index] for found_tag_index in res.index]
# print(f'found tags from tfidf: {res}')
if (not filter_tags):
return res
else:
return [tag for tag in res if tag in common_tags]
# test
index = 2
docs.reduced_body[index]
predict_tfidf(docs.reduced_body[index], False) #show all tags found
predict_tfidf(docs.reduced_body[index]) # show only tags found which are part of currated tags (list of 200)
# predicition - nouvelle methode
# TODO optimize - temps de calcul trop long ~ 3'30
def predict_tfidf(doc, filter_tags=True) -> list[str]:
"""
Returns a list of tags representative to the document provided, predicted from tf-idf
Parameters
----------
doc: iterable
a list of words to compute tf-idf
filter_tags: boolean
defines if returned tags must be part of a currated list of tags
Returns
-------
ret : a list of strings, may be empty
"""
res = vectorizer.transform([doc]).T.todense()
res = pd.DataFrame({'tfidf': res.tolist()}).sort_values('tfidf', ascending=False)[:5]
res = [ vectorizer.get_feature_names_out()[found_tag_index] for found_tag_index in res.index]
# print(f'found tags from tfidf: {res}')
if (not filter_tags):
return res
else:
return [tag for tag in res if tag in common_tags]
# test
index = 2
docs.reduced_body[index]
predict_tfidf(docs.reduced_body[index], False) #show all tags found
predict_tfidf(docs.reduced_body[index]) # show only tags found which are part of currated tags (list of 200)
Out[33]:
'linq query trying perform linq query object finding query example allowed get something like working linq query allowed'
Out[33]:
['linq', 'query', 'allowed', 'finding', 'perform']
Out[33]:
['linq']
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docs['predicted_tags_tfidf'] = [predict_tfidf(doc) for doc in docs.reduced_body]
docs['predicted_tags_tfidf'] = [predict_tfidf(doc) for doc in docs.reduced_body]
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# compute mlb of predicted tags
# docs['mlb_tags_pred_tfidf'] = [mlb.transform([tags]) for tags in docs.predicted_tags_tfidf]
docs['mlb_tags_pred_tfidf'] = mlb_b.transform(docs.predicted_tags_tfidf).tolist()
# compute mlb of predicted tags
# docs['mlb_tags_pred_tfidf'] = [mlb.transform([tags]) for tags in docs.predicted_tags_tfidf]
docs['mlb_tags_pred_tfidf'] = mlb_b.transform(docs.predicted_tags_tfidf).tolist()
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len(mlb_b.classes_.tolist())
# docs.mlb_tags_pred_tfidf[0]
len(mlb_b.classes_.tolist())
# docs.mlb_tags_pred_tfidf[0]
Out[36]:
1102
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from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report
from sklearn.metrics import average_precision_score
from sklearn.metrics import recall_score
y_true = mlb_b.transform(docs.tags).tolist()
y_pred = mlb_b.transform(docs.predicted_tags_tfidf).tolist()
accuracy_score(y_true, y_pred)
average_precision_score(y_true, y_pred)
# recall_score(y_true, y_pred, average='macro')
print(classification_report(y_true, y_pred, target_names=mlb_b.classes_))
from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report
from sklearn.metrics import average_precision_score
from sklearn.metrics import recall_score
y_true = mlb_b.transform(docs.tags).tolist()
y_pred = mlb_b.transform(docs.predicted_tags_tfidf).tolist()
accuracy_score(y_true, y_pred)
average_precision_score(y_true, y_pred)
# recall_score(y_true, y_pred, average='macro')
print(classification_report(y_true, y_pred, target_names=mlb_b.classes_))
Out[37]:
0.06417723385432025
Out[37]:
nan
precision recall f1-score support
.net 0.07 0.01 0.01 1402
able 0.00 0.00 0.00 0
access 0.00 0.00 0.00 0
according 0.00 0.00 0.00 0
account 0.00 0.00 0.00 0
achieve 0.00 0.00 0.00 0
across 0.00 0.00 0.00 0
action 0.00 0.00 0.00 0
active 0.00 0.00 0.00 0
activity 0.00 0.00 0.00 0
actual 0.00 0.00 0.00 0
actually 0.00 0.00 0.00 0
add 0.00 0.00 0.00 0
added 0.00 0.00 0.00 0
adding 0.00 0.00 0.00 0
additional 0.00 0.00 0.00 0
address 0.00 0.00 0.00 0
advance 0.00 0.00 0.00 0
advantage 0.00 0.00 0.00 0
advice 0.00 0.00 0.00 0
ajax 0.74 0.44 0.55 318
algorithm 0.61 0.29 0.40 474
allow 0.00 0.00 0.00 0
allowed 0.00 0.00 0.00 0
allows 0.00 0.00 0.00 0
almost 0.00 0.00 0.00 0
along 0.00 0.00 0.00 0
already 0.00 0.00 0.00 0
also 0.00 0.00 0.00 0
alternative 0.00 0.00 0.00 0
although 0.00 0.00 0.00 0
always 0.00 0.00 0.00 0
amazon-web-services 0.00 0.00 0.00 138
amount 0.00 0.00 0.00 0
android 0.95 0.32 0.48 1994
android-layout 0.00 0.00 0.00 158
android-studio 0.00 0.00 0.00 185
angular 0.62 0.62 0.62 173
angularjs 0.94 0.41 0.57 264
animation 0.44 0.68 0.54 112
annotation 0.00 0.00 0.00 0
another 0.00 0.00 0.00 0
answer 0.00 0.00 0.00 0
anybody 0.00 0.00 0.00 0
anyone 0.00 0.00 0.00 0
anything 0.00 0.00 0.00 0
anyway 0.00 0.00 0.00 0
apache 0.52 0.43 0.47 167
apache-spark 0.00 0.00 0.00 166
apache-spark-sql 0.00 0.00 0.00 99
api 0.20 0.49 0.28 147
app 0.00 0.00 0.00 0
appear 0.00 0.00 0.00 0
appears 0.00 0.00 0.00 0
apple 0.00 0.00 0.00 0
application 0.00 0.00 0.00 0
apply 0.00 0.00 0.00 0
appreciate 0.00 0.00 0.00 0
appreciated 0.00 0.00 0.00 0
approach 0.00 0.00 0.00 0
appropriate 0.00 0.00 0.00 0
apps 0.00 0.00 0.00 0
architecture 0.20 0.20 0.20 99
area 0.00 0.00 0.00 0
argument 0.00 0.00 0.00 0
around 0.00 0.00 0.00 0
array 0.00 0.00 0.00 0
arrays 0.00 0.00 0.00 433
article 0.00 0.00 0.00 0
ask 0.00 0.00 0.00 0
asked 0.00 0.00 0.00 0
asking 0.00 0.00 0.00 0
asp.net 0.00 0.00 0.00 579
asp.net-core 0.00 0.00 0.00 120
asp.net-mvc 0.00 0.00 0.00 500
asp.net-mvc-3 0.00 0.00 0.00 131
asp.net-mvc-4 0.00 0.00 0.00 110
asp.net-web-api 0.00 0.00 0.00 121
assembly 0.35 0.31 0.33 173
assume 0.00 0.00 0.00 0
async 0.00 0.00 0.00 0
async-await 0.00 0.00 0.00 139
asynchronous 0.55 0.23 0.33 206
attempt 0.00 0.00 0.00 0
attribute 0.00 0.00 0.00 0
audio 0.50 0.61 0.55 108
authentication 0.46 0.40 0.43 219
auto 0.00 0.00 0.00 0
automatically 0.00 0.00 0.00 0
available 0.00 0.00 0.00 0
avoid 0.00 0.00 0.00 0
aware 0.00 0.00 0.00 0
back 0.00 0.00 0.00 0
backend 0.00 0.00 0.00 0
background 0.00 0.00 0.00 0
bad 0.00 0.00 0.00 0
bar 0.00 0.00 0.00 0
base 0.00 0.00 0.00 0
based 0.00 0.00 0.00 0
bash 0.80 0.38 0.51 373
basic 0.00 0.00 0.00 0
basically 0.00 0.00 0.00 0
batch 0.00 0.00 0.00 0
bean 0.00 0.00 0.00 0
behavior 0.00 0.00 0.00 0
behind 0.00 0.00 0.00 0
believe 0.00 0.00 0.00 0
benefit 0.00 0.00 0.00 0
best 0.00 0.00 0.00 0
better 0.00 0.00 0.00 0
big 0.00 0.00 0.00 0
binary 0.00 0.00 0.00 0
bind 0.00 0.00 0.00 0
binding 0.00 0.00 0.00 0
bit 0.00 0.00 0.00 0
block 0.00 0.00 0.00 0
blog 0.00 0.00 0.00 0
book 0.00 0.00 0.00 0
boost 0.00 0.00 0.00 0
boot 0.00 0.00 0.00 0
bootstrap 0.00 0.00 0.00 0
bottom 0.00 0.00 0.00 0
box 0.00 0.00 0.00 0
branch 0.00 0.00 0.00 0
break 0.00 0.00 0.00 0
browser 0.18 0.43 0.26 108
buffer 0.00 0.00 0.00 0
bug 0.00 0.00 0.00 0
build 0.17 0.52 0.26 101
building 0.00 0.00 0.00 0
built 0.00 0.00 0.00 0
bundle 0.00 0.00 0.00 0
button 0.00 0.00 0.00 0
byte 0.00 0.00 0.00 0
c 0.00 0.00 0.00 915
c# 0.00 0.00 0.00 2644
c++ 0.00 0.00 0.00 1820
c++11 0.00 0.00 0.00 361
ca 0.00 0.00 0.00 0
cache 0.00 0.00 0.00 0
caching 0.60 0.27 0.37 134
calculate 0.00 0.00 0.00 0
call 0.00 0.00 0.00 0
callback 0.00 0.00 0.00 0
called 0.00 0.00 0.00 0
calling 0.00 0.00 0.00 0
came 0.00 0.00 0.00 0
camera 0.00 0.00 0.00 0
case 0.00 0.00 0.00 0
cause 0.00 0.00 0.00 0
cell 0.00 0.00 0.00 0
certain 0.00 0.00 0.00 0
certificate 0.00 0.00 0.00 0
change 0.00 0.00 0.00 0
changed 0.00 0.00 0.00 0
changing 0.00 0.00 0.00 0
char 0.00 0.00 0.00 0
character 0.00 0.00 0.00 0
check 0.00 0.00 0.00 0
checked 0.00 0.00 0.00 0
child 0.00 0.00 0.00 0
choice 0.00 0.00 0.00 0
choose 0.00 0.00 0.00 0
chrome 0.00 0.00 0.00 0
class 0.20 0.72 0.31 188
clear 0.00 0.00 0.00 0
click 0.00 0.00 0.00 0
client 0.00 0.00 0.00 0
close 0.00 0.00 0.00 0
cocoa 0.54 0.13 0.21 198
cocoa-touch 0.00 0.00 0.00 498
code 0.00 0.00 0.00 0
collection 0.00 0.00 0.00 0
color 0.00 0.00 0.00 0
colors 0.00 0.00 0.00 99
column 0.00 0.00 0.00 0
combination 0.00 0.00 0.00 0
come 0.00 0.00 0.00 0
command 0.00 0.00 0.00 0
command-line 0.00 0.00 0.00 146
comment 0.00 0.00 0.00 0
common 0.00 0.00 0.00 0
compare 0.00 0.00 0.00 0
comparison 0.00 0.00 0.00 0
compile 0.00 0.00 0.00 0
compiled 0.00 0.00 0.00 0
compiler 0.00 0.00 0.00 0
complete 0.00 0.00 0.00 0
completely 0.00 0.00 0.00 0
complex 0.00 0.00 0.00 0
component 0.00 0.00 0.00 0
computer 0.00 0.00 0.00 0
concept 0.00 0.00 0.00 0
concurrency 0.35 0.07 0.12 154
condition 0.00 0.00 0.00 0
config 0.00 0.00 0.00 0
configuration 0.19 0.27 0.22 98
configure 0.00 0.00 0.00 0
confused 0.00 0.00 0.00 0
connect 0.00 0.00 0.00 0
connection 0.00 0.00 0.00 0
consider 0.00 0.00 0.00 0
console 0.00 0.00 0.00 0
constant 0.00 0.00 0.00 0
constraint 0.00 0.00 0.00 0
constructor 0.00 0.00 0.00 0
contain 0.00 0.00 0.00 0
container 0.00 0.00 0.00 0
containing 0.00 0.00 0.00 0
contains 0.00 0.00 0.00 0
content 0.00 0.00 0.00 0
context 0.00 0.00 0.00 0
control 0.00 0.00 0.00 0
controller 0.00 0.00 0.00 0
convert 0.00 0.00 0.00 0
cookie 0.00 0.00 0.00 0
cookies 0.00 0.00 0.00 99
coordinate 0.00 0.00 0.00 0
copy 0.00 0.00 0.00 0
core 0.00 0.00 0.00 0
correct 0.00 0.00 0.00 0
correctly 0.00 0.00 0.00 0
could 0.00 0.00 0.00 0
count 0.00 0.00 0.00 0
couple 0.00 0.00 0.00 0
course 0.00 0.00 0.00 0
cpu 0.00 0.00 0.00 0
crash 0.00 0.00 0.00 0
create 0.00 0.00 0.00 0
created 0.00 0.00 0.00 0
creates 0.00 0.00 0.00 0
creating 0.00 0.00 0.00 0
cs 0.00 0.00 0.00 0
css 0.83 0.02 0.04 727
csv 0.62 0.69 0.65 107
current 0.00 0.00 0.00 0
currently 0.00 0.00 0.00 0
custom 0.00 0.00 0.00 0
d 0.00 0.00 0.00 0
data 0.00 0.00 0.00 0
data-structures 0.00 0.00 0.00 137
database 0.31 0.30 0.30 455
database-design 0.00 0.00 0.00 118
dataframe 0.60 0.54 0.57 236
date 0.43 0.74 0.54 154
datetime 0.71 0.43 0.54 182
day 0.00 0.00 0.00 0
db 0.00 0.00 0.00 0
deal 0.00 0.00 0.00 0
debug 0.00 0.00 0.00 0
debugging 0.38 0.18 0.24 197
decided 0.00 0.00 0.00 0
deep-learning 0.00 0.00 0.00 107
default 0.00 0.00 0.00 0
define 0.00 0.00 0.00 0
defined 0.00 0.00 0.00 0
definition 0.00 0.00 0.00 0
delegate 0.00 0.00 0.00 0
delete 0.00 0.00 0.00 0
dependency 0.00 0.00 0.00 0
dependency-injection 0.00 0.00 0.00 102
deploy 0.00 0.00 0.00 0
described 0.00 0.00 0.00 0
design 0.00 0.00 0.00 0
design-patterns 0.00 0.00 0.00 144
desktop 0.00 0.00 0.00 0
detail 0.00 0.00 0.00 0
detect 0.00 0.00 0.00 0
determine 0.00 0.00 0.00 0
developer 0.00 0.00 0.00 0
developing 0.00 0.00 0.00 0
development 0.00 0.00 0.00 0
device 0.00 0.00 0.00 0
dialog 0.00 0.00 0.00 0
dictionary 0.53 0.58 0.56 126
difference 0.00 0.00 0.00 0
different 0.00 0.00 0.00 0
direction 0.00 0.00 0.00 0
directly 0.00 0.00 0.00 0
directory 0.00 0.00 0.00 0
disable 0.00 0.00 0.00 0
disk 0.00 0.00 0.00 0
display 0.00 0.00 0.00 0
displayed 0.00 0.00 0.00 0
div 0.00 0.00 0.00 0
django 0.98 0.66 0.79 366
dll 0.00 0.00 0.00 0
doc 0.00 0.00 0.00 0
docker 0.89 0.81 0.85 113
document 0.00 0.00 0.00 0
documentation 0.00 0.00 0.00 0
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spark 0.00 0.00 0.00 0
special 0.00 0.00 0.00 0
specific 0.00 0.00 0.00 0
specifically 0.00 0.00 0.00 0
specified 0.00 0.00 0.00 0
specify 0.00 0.00 0.00 0
speed 0.00 0.00 0.00 0
spring 0.84 0.58 0.69 538
spring-boot 0.00 0.00 0.00 185
spring-mvc 0.00 0.00 0.00 188
sql 0.52 0.28 0.36 657
sql-server 0.00 0.00 0.00 378
sql-server-2008 0.00 0.00 0.00 106
sqlite 0.82 0.53 0.64 111
ssl 0.80 0.50 0.61 204
stack 0.00 0.00 0.00 0
stackoverflow 0.00 0.00 0.00 0
standard 0.00 0.00 0.00 0
start 0.00 0.00 0.00 0
started 0.00 0.00 0.00 0
starting 0.00 0.00 0.00 0
state 0.00 0.00 0.00 0
statement 0.00 0.00 0.00 0
static 0.00 0.00 0.00 0
status 0.00 0.00 0.00 0
std 0.00 0.00 0.00 0
step 0.00 0.00 0.00 0
still 0.00 0.00 0.00 0
stl 0.73 0.30 0.43 105
stop 0.00 0.00 0.00 0
storage 0.00 0.00 0.00 0
store 0.00 0.00 0.00 0
stored 0.00 0.00 0.00 0
stream 0.00 0.00 0.00 0
string 0.41 0.62 0.49 462
structure 0.00 0.00 0.00 0
stuck 0.00 0.00 0.00 0
studio 0.00 0.00 0.00 0
stuff 0.00 0.00 0.00 0
style 0.00 0.00 0.00 0
success 0.00 0.00 0.00 0
successfully 0.00 0.00 0.00 0
suggest 0.00 0.00 0.00 0
suggested 0.00 0.00 0.00 0
suggestion 0.00 0.00 0.00 0
support 0.00 0.00 0.00 0
supported 0.00 0.00 0.00 0
suppose 0.00 0.00 0.00 0
supposed 0.00 0.00 0.00 0
sure 0.00 0.00 0.00 0
swift 0.94 0.39 0.55 635
switch 0.00 0.00 0.00 0
symbol 0.00 0.00 0.00 0
syntax 0.16 0.26 0.20 100
system 0.00 0.00 0.00 0
tab 0.00 0.00 0.00 0
table 0.00 0.00 0.00 0
tag 0.00 0.00 0.00 0
take 0.00 0.00 0.00 0
taking 0.00 0.00 0.00 0
target 0.00 0.00 0.00 0
task 0.00 0.00 0.00 0
tell 0.00 0.00 0.00 0
template 0.00 0.00 0.00 0
templates 0.00 0.00 0.00 125
tensorflow 0.97 0.54 0.69 112
term 0.00 0.00 0.00 0
terminal 0.42 0.52 0.47 115
test 0.00 0.00 0.00 0
tested 0.00 0.00 0.00 0
testing 0.24 0.20 0.22 155
text 0.00 0.00 0.00 0
thank 0.00 0.00 0.00 0
thanks 0.00 0.00 0.00 0
therefore 0.00 0.00 0.00 0
thing 0.00 0.00 0.00 0
think 0.00 0.00 0.00 0
thinking 0.00 0.00 0.00 0
though 0.00 0.00 0.00 0
thought 0.00 0.00 0.00 0
thread 0.00 0.00 0.00 0
three 0.00 0.00 0.00 0
throw 0.00 0.00 0.00 0
thus 0.00 0.00 0.00 0
time 0.00 0.00 0.00 0
title 0.00 0.00 0.00 0
together 0.00 0.00 0.00 0
token 0.00 0.00 0.00 0
tomcat 0.73 0.75 0.74 106
tool 0.00 0.00 0.00 0
top 0.00 0.00 0.00 0
topic 0.00 0.00 0.00 0
total 0.00 0.00 0.00 0
touch 0.00 0.00 0.00 0
transaction 0.00 0.00 0.00 0
tree 0.00 0.00 0.00 0
tried 0.00 0.00 0.00 0
trouble 0.00 0.00 0.00 0
true 0.00 0.00 0.00 0
try 0.00 0.00 0.00 0
trying 0.00 0.00 0.00 0
tsql 0.79 0.10 0.17 156
turn 0.00 0.00 0.00 0
tutorial 0.00 0.00 0.00 0
twitter-bootstrap 0.00 0.00 0.00 161
two 0.00 0.00 0.00 0
type 0.00 0.00 0.00 0
types 0.00 0.00 0.00 117
typescript 0.91 0.30 0.46 138
ubuntu 0.58 0.42 0.49 189
ui 0.00 0.00 0.00 0
uikit 0.62 0.07 0.12 118
uitableview 0.81 0.42 0.56 160
uiview 0.58 0.55 0.57 134
unable 0.00 0.00 0.00 0
understand 0.00 0.00 0.00 0
understanding 0.00 0.00 0.00 0
unfortunately 0.00 0.00 0.00 0
unicode 0.83 0.49 0.61 127
unique 0.00 0.00 0.00 0
unit 0.00 0.00 0.00 0
unit-testing 0.00 0.00 0.00 295
unix 0.52 0.21 0.30 218
update 0.00 0.00 0.00 0
updated 0.00 0.00 0.00 0
upload 0.00 0.00 0.00 0
url 0.27 0.73 0.39 108
us 0.00 0.00 0.00 0
usage 0.00 0.00 0.00 0
use 0.00 0.00 0.00 0
used 0.00 0.00 0.00 0
useful 0.00 0.00 0.00 0
user 0.00 0.00 0.00 0
user-interface 0.00 0.00 0.00 142
username 0.00 0.00 0.00 0
using 0.00 0.00 0.00 0
usually 0.00 0.00 0.00 0
utf 0.00 0.00 0.00 0
v 0.00 0.00 0.00 0
valid 0.00 0.00 0.00 0
validation 0.46 0.57 0.51 113
value 0.00 0.00 0.00 0
variable 0.00 0.00 0.00 0
variables 0.00 0.00 0.00 108
various 0.00 0.00 0.00 0
ve 0.00 0.00 0.00 0
vector 0.00 0.00 0.00 0
version 0.00 0.00 0.00 0
via 0.00 0.00 0.00 0
video 0.37 0.80 0.50 126
view 0.00 0.00 0.00 0
virtual 0.00 0.00 0.00 0
visible 0.00 0.00 0.00 0
visual 0.00 0.00 0.00 0
visual-studio 0.00 0.00 0.00 269
visual-studio-2010 0.00 0.00 0.00 110
vswait 0.00 0.00 0.00 0
want 0.00 0.00 0.00 0
wanted 0.00 0.00 0.00 0
warning 0.00 0.00 0.00 0
way 0.00 0.00 0.00 0
web 0.00 0.00 0.00 0
web-services 0.00 0.00 0.00 146
website 0.00 0.00 0.00 0
well 0.00 0.00 0.00 0
whenever 0.00 0.00 0.00 0
whether 0.00 0.00 0.00 0
whole 0.00 0.00 0.00 0
width 0.00 0.00 0.00 0
window 0.00 0.00 0.00 0
windows 0.25 0.01 0.01 505
winforms 0.81 0.19 0.31 130
within 0.00 0.00 0.00 0
without 0.00 0.00 0.00 0
wo 0.00 0.00 0.00 0
wondering 0.00 0.00 0.00 0
word 0.00 0.00 0.00 0
work 0.00 0.00 0.00 0
worked 0.00 0.00 0.00 0
working 0.00 0.00 0.00 0
world 0.00 0.00 0.00 0
would 0.00 0.00 0.00 0
wpf 0.94 0.48 0.64 329
write 0.00 0.00 0.00 0
writing 0.00 0.00 0.00 0
written 0.00 0.00 0.00 0
wrong 0.00 0.00 0.00 0
x 0.00 0.00 0.00 0
x86 0.00 0.00 0.00 155
xaml 0.69 0.36 0.48 119
xcode 0.71 0.32 0.44 632
xml 0.54 0.54 0.54 207
year 0.00 0.00 0.00 0
yes 0.00 0.00 0.00 0
yet 0.00 0.00 0.00 0
micro avg 0.55 0.22 0.31 64905
macro avg 0.08 0.05 0.05 64905
weighted avg 0.51 0.22 0.26 64905
samples avg 0.36 0.22 0.25 64905
Conclusion¶
Using tf-idf is not really performant to predict relevant tags (F1 score is way lower than 1)
| Score | value |
|---|---|
| Precision score | 0.41 |
| Recall score | 0.23 |
| f1-score | 0.26 |
average parameter is required for multiclass/multilabel targets.
'None' : the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data:
'micro': Calculate metrics globally by counting the total true positives, false negatives and false positives.
'macro': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
'weighted': Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters ‘macro’ to account for label imbalance; it can result in an F-score that is not between precision and recall.Plus le recall est haut, plus le modèle repère de positif.
Plus la precision est haute, moins le modèle se trompe sur les positifs
Le F1 Score permet d’effectuer une bonne évaluation de la performance de notre modèle
reminder : https://inside-machinelearning.com/recall-precision-f1-score/
LDA¶
In [38]:
Copied!
from sklearn.decomposition import LatentDirichletAllocation
lda = LatentDirichletAllocation(n_components=1000, random_state=0, n_jobs=-1)
lda.fit(bow)
from sklearn.decomposition import LatentDirichletAllocation
lda = LatentDirichletAllocation(n_components=1000, random_state=0, n_jobs=-1)
lda.fit(bow)
Out[38]:
LatentDirichletAllocation(n_components=1000, n_jobs=-1, random_state=0)
In [39]:
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for idx, topic in enumerate(lda.components_):
print ("Topic ", idx, " ".join(cv.get_feature_names()[i] for i in topic.argsort()[:-3 -1:-1]))
for idx, topic in enumerate(lda.components_):
print ("Topic ", idx, " ".join(cv.get_feature_names()[i] for i in topic.argsort()[:-3 -1:-1]))
Topic 0 statement using nt Topic 1 div instead like Topic 2 target error setting Topic 3 almost nt answer Topic 4 code including saying Topic 5 opencv using code Topic 6 task run way Topic 7 browser firefox work Topic 8 numpy like python Topic 9 bean spring created Topic 10 php use using Topic 11 happens fix problem Topic 12 lock code using Topic 13 whether changed really Topic 14 read reading file Topic 15 obvious nt seems Topic 16 project option ve Topic 17 remove want trying Topic 18 avoid like using Topic 19 vector way want Topic 20 way wrong using Topic 21 result get term Topic 22 actual really simple Topic 23 people program question Topic 24 le using problem Topic 25 project repository feature Topic 26 record disk sqlite Topic 27 appreciated would help Topic 28 may solution need Topic 29 rule installation able Topic 30 prevent since still Topic 31 buffer data using Topic 32 great would work Topic 33 design let like Topic 34 logic code use Topic 35 google using get Topic 36 module use using Topic 37 information regarding could Topic 38 range appropriate would Topic 39 protocol http use Topic 40 safe thread use Topic 41 link http want Topic 42 import file error Topic 43 client server need Topic 44 scale would like Topic 45 used set nt Topic 46 second first two Topic 47 understanding correct access Topic 48 valid answer may Topic 49 batch tried way Topic 50 task system ve Topic 51 platform would provide Topic 52 somehow ll time Topic 53 port one nt Topic 54 camera learn get Topic 55 microsoft nt using Topic 56 longer seems since Topic 57 django tried ve Topic 58 simulator io xcode Topic 59 run running code Topic 60 simple python text Topic 61 moment like need Topic 62 character would need Topic 63 wondering way would Topic 64 security without see Topic 65 menu option want Topic 66 client make need Topic 67 download file using Topic 68 pattern use example Topic 69 db get need Topic 70 like could way Topic 71 model data using Topic 72 ui make need Topic 73 rail gem ve Topic 74 specifically inheritance example Topic 75 happening problem work Topic 76 install pip using Topic 77 different two fact Topic 78 error problem ii Topic 79 user want able Topic 80 scroll nt scrolling Topic 81 flutter error trying Topic 82 cs like possible Topic 83 column data want Topic 84 game would nt Topic 85 implementing implement way Topic 86 configuration file error Topic 87 exception code throw Topic 88 one got boost Topic 89 encryption data code Topic 90 feature use new Topic 91 nt know good Topic 92 count number using Topic 93 part stuff basically Topic 94 installing install even Topic 95 url http get Topic 96 efficient downloaded way Topic 97 date supported using Topic 98 current get need Topic 99 shell work get Topic 100 want created set Topic 101 explain someone please Topic 102 purpose case use Topic 103 question going found Topic 104 npm install error Topic 105 project maven use Topic 106 id facebook get Topic 107 math code value Topic 108 reference want website Topic 109 minute every time Topic 110 writing using write Topic 111 yes case nt Topic 112 effect like tried Topic 113 day year get Topic 114 get use way Topic 115 firefox chrome fine Topic 116 edit able etc Topic 117 environment running following Topic 118 exist happen might Topic 119 also still answer Topic 120 delete new would Topic 121 switch case nt Topic 122 mvvm using code Topic 123 plot bootstrap like Topic 124 appears way used Topic 125 symbol make used Topic 126 migration database change Topic 127 may code question Topic 128 go would way Topic 129 app file one Topic 130 passing another seems Topic 131 socket method call Topic 132 api using successfully Topic 133 http edit add Topic 134 matplotlib call since Topic 135 useful haskell example Topic 136 code loop callback Topic 137 parent child get Topic 138 resource following good Topic 139 certain like nt Topic 140 warning error get Topic 141 doc used done Topic 142 mind would like Topic 143 free kernel question Topic 144 xcode sdk project Topic 145 compiler make question Topic 146 faster code time Topic 147 short seems particular Topic 148 thing update ve Topic 149 solution data row Topic 150 mobile also like Topic 151 code without tried Topic 152 background seems working Topic 153 program run work Topic 154 data file byte Topic 155 expect work going Topic 156 keyboard like nt Topic 157 excel file using Topic 158 easy one rather Topic 159 array following would Topic 160 external without need Topic 161 saying code way Topic 162 row specific one Topic 163 along far one Topic 164 chrome http ie Topic 165 instruction code first Topic 166 back work going Topic 167 aware also however Topic 168 limit multithreading used Topic 169 creates file including Topic 170 code exactly note Topic 171 linq combination like Topic 172 copy file another Topic 173 shown however code Topic 174 public using use Topic 175 using error edit Topic 176 algorithm one find Topic 177 document hand like Topic 178 heap ve use Topic 179 http request using Topic 180 best practice way Topic 181 modify similar need Topic 182 frame like code Topic 183 std use using Topic 184 requirement possible using Topic 185 index get example Topic 186 csv data example Topic 187 constructor nt call Topic 188 inside outside scope Topic 189 desktop application quite Topic 190 dll nt code Topic 191 iphone app want Topic 192 length concept understand Topic 193 ie work nt Topic 194 somewhere see actually Topic 195 reference say found Topic 196 require answer may Topic 197 native dom code Topic 198 basic know really Topic 199 sample bug code Topic 200 usually feel used Topic 201 data problem using Topic 202 eclipse work nt Topic 203 must know something Topic 204 assume would nt Topic 205 question answer next Topic 206 connect socket server Topic 207 consider following case Topic 208 appear get problem Topic 209 stack question know Topic 210 service using call Topic 211 answer draw make Topic 212 nt take ve Topic 213 element way find Topic 214 attempt however error Topic 215 dependency included example Topic 216 exists already given Topic 217 trouble ve getting Topic 218 function call code Topic 219 block code use Topic 220 information one point Topic 221 tested currently correct Topic 222 char know edit Topic 223 xml file using Topic 224 common seems code Topic 225 architecture ios work Topic 226 attribute nt like Topic 227 appreciate really ve Topic 228 address get possible Topic 229 github project nt Topic 230 macos one using Topic 231 microsoft server sql Topic 232 top like see Topic 233 final different actual Topic 234 apple couple fix Topic 235 react using use Topic 236 fails work error Topic 237 blog post use Topic 238 console error see Topic 239 title question need Topic 240 sql server using Topic 241 error nt page Topic 242 determine way using Topic 243 instance page login Topic 244 data case using Topic 245 bind want way Topic 246 want example need Topic 247 window way using Topic 248 required following error Topic 249 check whether need Topic 250 scenario question would Topic 251 domain object class Topic 252 register used code Topic 253 believe whole get Topic 254 true read correct Topic 255 file would register Topic 256 static nt make Topic 257 node using one Topic 258 datetime time get Topic 259 completely work nt Topic 260 made question know Topic 261 lambda code using Topic 262 custom code nt Topic 263 uiview everything nothing Topic 264 cause said problem Topic 265 token request access Topic 266 binding data show Topic 267 next put take Topic 268 matter amount always Topic 269 site one different Topic 270 get code nt Topic 271 ipad io see Topic 272 key way nt Topic 273 tab code different Topic 274 select route like Topic 275 server user access Topic 276 image file code Topic 277 exact get exactly Topic 278 postgresql error used Topic 279 font use different Topic 280 microsoft might code Topic 281 label like code Topic 282 implement implemented use Topic 283 correct system far Topic 284 direction right point Topic 285 like nt use Topic 286 file binary general Topic 287 article question answer Topic 288 value could please Topic 289 complex simple need Topic 290 two example data Topic 291 nt wo work Topic 292 installed install get Topic 293 data example would Topic 294 api call use Topic 295 nt get work Topic 296 dialog show problem Topic 297 idea ve wrong Topic 298 provider use sure Topic 299 location update even Topic 300 hash time come Topic 301 add starting code Topic 302 internet nt use Topic 303 prefer would case Topic 304 pointer would like Topic 305 existing new add Topic 306 core ve find Topic 307 whenever try code Topic 308 file read data Topic 309 line way file Topic 310 add want serialization Topic 311 syntax using like Topic 312 library using use Topic 313 generated since automatically Topic 314 device work nt Topic 315 access possible another Topic 316 supposed work get Topic 317 procedure stored using Topic 318 number random example Topic 319 jar executable file Topic 320 file upload using Topic 321 slow time take Topic 322 manage like really Topic 323 asynchronous one use Topic 324 source code eclipse Topic 325 call calling code Topic 326 manually bundle every Topic 327 field like using Topic 328 try solution want Topic 329 bit code use Topic 330 debugging debug code Topic 331 use number environment Topic 332 enable disable still Topic 333 use format currently Topic 334 cache caching used Topic 335 overflow stack question Topic 336 git local file Topic 337 first working would Topic 338 software come etc Topic 339 result type nt Topic 340 nt find fine Topic 341 space take two Topic 342 pdf file using Topic 343 advance thanks help Topic 344 resource nice get Topic 345 defined define following Topic 346 called never first Topic 347 time ve code Topic 348 spring boot using Topic 349 view like get Topic 350 code javascript value Topic 351 use edit see Topic 352 wpf like application Topic 353 enabled fact etc Topic 354 extract file using Topic 355 call thing model Topic 356 online found find Topic 357 tried correctly using Topic 358 vector would using Topic 359 file get php Topic 360 previous question way Topic 361 schema using update Topic 362 audio using question Topic 363 data core host Topic 364 added code using Topic 365 selenium using trying Topic 366 would nt like Topic 367 button click want Topic 368 structure data use Topic 369 fragment inside using Topic 370 level would way Topic 371 general point concept Topic 372 regex like find Topic 373 build project file Topic 374 save mac file Topic 375 file want one Topic 376 file also using Topic 377 given file would Topic 378 https another error Topic 379 memory control browser Topic 380 tutorial nt know Topic 381 function would create Topic 382 private like nt Topic 383 dictionary retrieve way Topic 384 handler nt want Topic 385 perhaps something seem Topic 386 general nt much Topic 387 relevant using create Topic 388 cpu system show Topic 389 value method use Topic 390 code nt way Topic 391 one therefore would Topic 392 turn simple know Topic 393 properly example lot Topic 394 proper way like Topic 395 directory file find Topic 396 going call one Topic 397 using source ve Topic 398 core data use Topic 399 uikit use ve Topic 400 certificate file ca Topic 401 object nt one Topic 402 audio api data Topic 403 person uiview uitableview Topic 404 implementation real different Topic 405 research answer question Topic 406 data nt program Topic 407 stackoverflow working code Topic 408 site also server Topic 409 proxy server work Topic 410 bit might use Topic 411 parameter microsoft know Topic 412 layout make different Topic 413 div see work Topic 414 easier making use Topic 415 ii error running Topic 416 session created need Topic 417 python quite question Topic 418 test run nt Topic 419 mvc using project Topic 420 sometimes executed problem Topic 421 key using certificate Topic 422 last showing show Topic 423 project version file Topic 424 authentication security problem Topic 425 helpful would nt Topic 426 article would may Topic 427 apps app know Topic 428 image using want Topic 429 future use lot Topic 430 comparison find one Topic 431 development easily could Topic 432 update version another Topic 433 http google reference Topic 434 would like nt Topic 435 redirect http work Topic 436 app using nt Topic 437 property use following Topic 438 position normal area Topic 439 hit place would Topic 440 book reading way Topic 441 build image docker Topic 442 repository session login Topic 443 list like would Topic 444 javascript using way Topic 445 based better used Topic 446 controller like would Topic 447 manager different use Topic 448 processing requires similar Topic 449 boost using use Topic 450 screen make want Topic 451 within like code Topic 452 delegate need nt Topic 453 mean operator exactly Topic 454 code result edit Topic 455 version nt handler Topic 456 empty reason always Topic 457 solution call node Topic 458 error getting tried Topic 459 creating created trying Topic 460 item one get Topic 461 tomcat running application Topic 462 optimization call make Topic 463 file encoding problem Topic 464 others say ve Topic 465 account create need Topic 466 video using nt Topic 467 store written used Topic 468 experience development good Topic 469 row like want Topic 470 followed ve tried Topic 471 log flag draw Topic 472 many thing would Topic 473 ii load express Topic 474 format configuration setting Topic 475 render way like Topic 476 without instruction run Topic 477 match using new Topic 478 adding add nt Topic 479 cookie request set Topic 480 header nt get Topic 481 cocoa seems thing Topic 482 related run update Topic 483 interface definition implement Topic 484 content something allow Topic 485 child nt make Topic 486 follow nt anyway Topic 487 handling way handle Topic 488 angularjs using get Topic 489 solution problem find Topic 490 change changing want Topic 491 related created using Topic 492 safari work browser Topic 493 text nt want Topic 494 example lot question Topic 495 anybody know help Topic 496 folder file put Topic 497 re note thing Topic 498 memory case much Topic 499 detail see error Topic 500 color like change Topic 501 networking like approach Topic 502 value get case Topic 503 perform would use Topic 504 entry point edit Topic 505 type like nt Topic 506 token browser would Topic 507 orm using looking Topic 508 push suppose want Topic 509 duplicate question possible Topic 510 return returning call Topic 511 ubuntu install work Topic 512 base using still Topic 513 full file could Topic 514 pull nt ve Topic 515 create using sure Topic 516 automatically would use Topic 517 model look like Topic 518 play code work Topic 519 database data would Topic 520 understanding understand nt Topic 521 way somehow directly Topic 522 stop working ve Topic 523 standard int question Topic 524 serialization nt ve Topic 525 sqlite must one Topic 526 control use nt Topic 527 branch working new Topic 528 validation error like Topic 529 authentication use using Topic 530 configure use http Topic 531 mode using work Topic 532 message must every Topic 533 single code another Topic 534 style unique way Topic 535 ve section configuration Topic 536 gradle report using Topic 537 sql transaction connection Topic 538 foo parsing like Topic 539 testing logging code Topic 540 returned call however Topic 541 guess nt still Topic 542 keep nt yet Topic 543 sending sent using Topic 544 checked throw benefit Topic 545 engine use using Topic 546 change version make Topic 547 control update make Topic 548 typescript code using Topic 549 host use would Topic 550 deal way seems Topic 551 network get using Topic 552 response request get Topic 553 noticed ve also Topic 554 separate seem answer Topic 555 path file get Topic 556 global one need Topic 557 info also question Topic 558 fast think pretty Topic 559 stored data etc Topic 560 condition two example Topic 561 framework use project Topic 562 android using code Topic 563 release version way Topic 564 null get code Topic 565 named resolve like Topic 566 opencv native information Topic 567 function case would Topic 568 example learn understand Topic 569 constraint nt correctly Topic 570 extension file possible Topic 571 large say nt Topic 572 long time take Topic 573 finding far etc Topic 574 contain file first Topic 575 java whether find Topic 576 passed one always Topic 577 concurrency using would Topic 578 comment visible like Topic 579 sense make produce Topic 580 annotation using use Topic 581 scrolling scroll work Topic 582 token one post Topic 583 layer network use Topic 584 output following example Topic 585 ok look think Topic 586 particular using following Topic 587 buffer time performance Topic 588 break like look Topic 589 issue could get Topic 590 data get would Topic 591 asking question like Topic 592 variable use like Topic 593 graph using example Topic 594 side note one Topic 595 notification app user Topic 596 height get set Topic 597 cell tried code Topic 598 jquery using work Topic 599 close behind code Topic 600 sort thing like Topic 601 correctly ve tried Topic 602 virtual create could Topic 603 guide deploy nt Topic 604 firebase developing using Topic 605 root tried without Topic 606 order relationship one Topic 607 size know set Topic 608 computer nt even Topic 609 call would need Topic 610 include file code Topic 611 machine running nt Topic 612 like need request Topic 613 via also currently Topic 614 open new found Topic 615 drop like work Topic 616 special every used Topic 617 repository one using Topic 618 section data specific Topic 619 sorting sort one Topic 620 remote server using Topic 621 detect way able Topic 622 time every get Topic 623 issue seems fix Topic 624 version use different Topic 625 nt take tag Topic 626 tell one push Topic 627 collection must two Topic 628 socket although request Topic 629 form using want Topic 630 profile using find Topic 631 string like way Topic 632 backend like would Topic 633 generic nt like Topic 634 allowed resource code Topic 635 template use way Topic 636 laravel make ve Topic 637 scala question nt Topic 638 debug loaded code Topic 639 matrix scipy using Topic 640 step first make Topic 641 io resource find Topic 642 unit code test Topic 643 tsql data use Topic 644 additional question every Topic 645 force use way Topic 646 handle need way Topic 647 assembly could one Topic 648 view one project Topic 649 support apache use Topic 650 suggested tried ve Topic 651 nt work re Topic 652 animation like nt Topic 653 process running time Topic 654 gui want time Topic 655 work problem provider Topic 656 except default instead Topic 657 calculate two way Topic 658 storage use user Topic 659 wanted know using Topic 660 connection possible application Topic 661 tensorflow work trying Topic 662 point several tree Topic 663 password using user Topic 664 java total code Topic 665 action equivalent using Topic 666 query join like Topic 667 oop git branch Topic 668 choice use would Topic 669 probably one different Topic 670 bundle nt would Topic 671 taking example note Topic 672 find answer swift Topic 673 little work bit Topic 674 available use using Topic 675 encoding unicode correctly Topic 676 alternative like use Topic 677 left right active Topic 678 could edit see Topic 679 world question nt Topic 680 async call use Topic 681 chrome browser way Topic 682 kind different seems Topic 683 merge displayed two Topic 684 ie constant whether Topic 685 rather example ve Topic 686 fixed still issue Topic 687 necessary would question Topic 688 try finally problem Topic 689 status navigation like Topic 690 setting also bar Topic 691 according unix answer Topic 692 show complete need Topic 693 resource would case Topic 694 code get behavior Topic 695 nt would say Topic 696 provides data use Topic 697 obviously problem probably Topic 698 domain different use Topic 699 binary using get Topic 700 gcc code use Topic 701 seen ve example Topic 702 operation may question Topic 703 replace one another Topic 704 column data new Topic 705 language like would Topic 706 panda data using Topic 707 thinking would think Topic 708 important first reason Topic 709 object like use Topic 710 must nt java Topic 711 difference two one Topic 712 instance created want Topic 713 compare two different Topic 714 studio visual project Topic 715 message error using Topic 716 returning better nt Topic 717 hour time every Topic 718 main problem one Topic 719 searching use way Topic 720 user way would Topic 721 work way need Topic 722 none several working Topic 723 new old since Topic 724 bottom see problem Topic 725 source build code Topic 726 active connection every Topic 727 put etc also Topic 728 cell problem one Topic 729 dynamic latest version Topic 730 double code two Topic 731 similar really something Topic 732 original problem nt Topic 733 false use always Topic 734 suggestion nt got Topic 735 domain person rule Topic 736 expected work nt Topic 737 answer work would Topic 738 usage use time Topic 739 site base good Topic 740 rest use example Topic 741 would also store Topic 742 unable explanation ve Topic 743 end would done Topic 744 scrolling content bottom Topic 745 data batch would Topic 746 etc using like Topic 747 performance using lot Topic 748 mentioned like error Topic 749 containing contains find Topic 750 docker container running Topic 751 reason use one Topic 752 specified specific wrong Topic 753 component using like Topic 754 touch code specific Topic 755 loop nt code Topic 756 achieve want like Topic 757 pixel way one Topic 758 yet far extract Topic 759 ran error code Topic 760 pro use one Topic 761 using complete know Topic 762 winforms reactjs application Topic 763 always way ssl Topic 764 flag exactly make Topic 765 phone problem need Topic 766 icon nt inside Topic 767 nt also local Topic 768 interested somehow also Topic 769 nt ve set Topic 770 language would way Topic 771 byte code nt Topic 772 editor use like Topic 773 permission user get Topic 774 needed using able Topic 775 token authentication using Topic 776 linux execute like Topic 777 product may different Topic 778 pretty code good Topic 779 follows look like Topic 780 ajax call request Topic 781 ssl http using Topic 782 angular using work Topic 783 need socket two Topic 784 queue work nt Topic 785 number new etc Topic 786 hibernate jpa using Topic 787 box mongodb using Topic 788 missing something anything Topic 789 auto new possible Topic 790 file add nt Topic 791 clear scroll inside Topic 792 global tree action Topic 793 decided question see Topic 794 tool use nt Topic 795 width piece height Topic 796 tree success without Topic 797 course would like Topic 798 map way want Topic 799 mysql using nt Topic 800 thus given must Topic 801 state idea call Topic 802 extra live way Topic 803 swift code using Topic 804 give could table Topic 805 apply use would Topic 806 installed package later Topic 807 application us want Topic 808 across separate come Topic 809 often without lot Topic 810 else something need Topic 811 thing would one Topic 812 default setting set Topic 813 route read anything Topic 814 dynamically created create Topic 815 receive problem case Topic 816 integer number nt Topic 817 content however file Topic 818 looked various ve Topic 819 dataframe panda like Topic 820 mb much take Topic 821 built way simply Topic 822 code computer like Topic 823 view problem apps Topic 824 answer database would Topic 825 removed correctly since Topic 826 context put use Topic 827 solve problem simple Topic 828 xaml like would Topic 829 option put question Topic 830 developer version rather Topic 831 script run way Topic 832 move functionality nt Topic 833 camera take hope Topic 834 advice could looking Topic 835 project setting website Topic 836 big need whole Topic 837 nt would without Topic 838 entity code data Topic 839 figure nt ca Topic 840 share would please Topic 841 member case example Topic 842 thing ve something Topic 843 started via see Topic 844 port one get Topic 845 fit nt something Topic 846 system one may Topic 847 event nt get Topic 848 behavior even well Topic 849 plugin know find Topic 850 website would idea Topic 851 load loading without Topic 852 approach better best Topic 853 print code using Topic 854 picture general always Topic 855 insert update using Topic 856 pas want work Topic 857 use point using Topic 858 set however correct Topic 859 code using library Topic 860 asked suggest answer Topic 861 django using get Topic 862 sound like nt Topic 863 specify using nt Topic 864 confused use used Topic 865 hard find need Topic 866 allows looking use Topic 867 filter using like Topic 868 small project currently Topic 869 like get found Topic 870 layer nt using Topic 871 appropriate know want Topic 872 name get nt Topic 873 per one want Topic 874 expression regular nt Topic 875 container use also Topic 876 managed etc whole Topic 877 documentation nt seem Topic 878 draw two point Topic 879 data excel solution Topic 880 relationship input would Topic 881 send need data Topic 882 section example code Topic 883 key use data Topic 884 topic question answer Topic 885 contains runtime code Topic 886 nested trying like Topic 887 javascript token cookie Topic 888 setup config file Topic 889 learning use understand Topic 890 terminal run work Topic 891 server code send Topic 892 bash file using Topic 893 fine work nothing Topic 894 callback code like Topic 895 class way want Topic 896 login nt new Topic 897 quite able problem Topic 898 wrong task version Topic 899 shared library used Topic 900 option quite ll Topic 901 bar like make Topic 902 selected get show Topic 903 activity android want Topic 904 transaction use make Topic 905 though would like Topic 906 view controller another Topic 907 table like data Topic 908 multiple one single Topic 909 ie work make Topic 910 sequence like would Topic 911 new code time Topic 912 python use code Topic 913 change want answer Topic 914 post data get Topic 915 recently use least Topic 916 bad nt ca Topic 917 model parameter like Topic 918 choose one use Topic 919 together use two Topic 920 failed error spark Topic 921 web time application Topic 922 described question nt Topic 923 want trying good Topic 924 group role like Topic 925 parse found ve Topic 926 display want able Topic 927 authentication nt get Topic 928 especially correct like Topic 929 provided provide given Topic 930 start find would Topic 931 connection working update Topic 932 effect fixed error Topic 933 stream use using Topic 934 would one example Topic 935 stuck get trying Topic 936 compiled code write Topic 937 programming code know Topic 938 method use using Topic 939 cache data even Topic 940 initial code thought Topic 941 crash app problem Topic 942 update updated still Topic 943 provide seems however Topic 944 mention nt answer Topic 945 nt engine android Topic 946 three present different Topic 947 transaction new connection Topic 948 convert way want Topic 949 command file run Topic 950 page nt way Topic 951 express thing ve Topic 952 speed goal since Topic 953 situation use question Topic 954 configuration ve data Topic 955 compile error code Topic 956 package install using Topic 957 due code nt Topic 958 thread question one Topic 959 production development use Topic 960 complete nt work Topic 961 bit exception stack Topic 962 unfortunately project changed Topic 963 point coordinate nt Topic 964 execution time code Topic 965 search find result Topic 966 allow ask nt Topic 967 database application heap Topic 968 worked fine get Topic 969 either entire would Topic 970 csv file trying Topic 971 using security whether Topic 972 building seems right Topic 973 stl standard nt Topic 974 utf encoding file Topic 975 install tried error Topic 976 code anything work Topic 977 web might via Topic 978 argument take nt Topic 979 generate way file Topic 980 around way nt Topic 981 tag one like Topic 982 job would want Topic 983 html code using Topic 984 server running need Topic 985 advantage ever use Topic 986 mentioned however first Topic 987 input take would Topic 988 nt make control Topic 989 word using well Topic 990 email using code Topic 991 seems event though Topic 992 maybe got already Topic 993 ll using correctly Topic 994 thank get trying Topic 995 write directly complete Topic 996 ruby rail like Topic 997 driver username used Topic 998 upload picture using Topic 999 json data using
In [40]:
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def predict_lda(text):
best_topic_indexes = lda.transform(cv.transform([text])).argsort()[0][::-1][0]
# print(f'lda best topic index found: {best_topic_indexes}')
best_topics = lda.components_[best_topic_indexes].argsort()[::-1]
# print(f'topic\'s word indexes {best_topics[:1]}')
ret = [cv.get_feature_names()[topic] for topic in best_topics[:1]]
# print(f'predicted tag: {ret}')
return ret
def predict_lda(text):
best_topic_indexes = lda.transform(cv.transform([text])).argsort()[0][::-1][0]
# print(f'lda best topic index found: {best_topic_indexes}')
best_topics = lda.components_[best_topic_indexes].argsort()[::-1]
# print(f'topic\'s word indexes {best_topics[:1]}')
ret = [cv.get_feature_names()[topic] for topic in best_topics[:1]]
# print(f'predicted tag: {ret}')
return ret
In [41]:
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# some indexes that work, otherwise most of the time predicted tag is wrong
# index = 7894
index = 1250
text = docs.reduced_body[index]
print(f'input text: {text}')
print(f'known tags: {docs.tags[index]}')
predict_lda(text)
# some indexes that work, otherwise most of the time predicted tag is wrong
# index = 7894
index = 1250
text = docs.reduced_body[index]
print(f'input text: {text}')
print(f'known tags: {docs.tags[index]}')
predict_lda(text)
input text: bit x machine code like first byte x disk file using suggestion linux file known tags: ['linux', 'assembly', 'x86']
Out[41]:
['linux']
LDA Scoring¶
In [42]:
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from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report
from sklearn.metrics import average_precision_score
MAX_COUNT = 1000
y_true = mlb_t.transform(docs.tags[:MAX_COUNT])
y_pred = mlb_t.transform([predict_lda(text) for text in docs.reduced_body[:MAX_COUNT]])
accuracy_score(y_true, y_pred)
average_precision_score(y_true, y_pred)
print(classification_report(y_true, y_pred, target_names=mlb_t.classes_))
from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report
from sklearn.metrics import average_precision_score
MAX_COUNT = 1000
y_true = mlb_t.transform(docs.tags[:MAX_COUNT])
y_pred = mlb_t.transform([predict_lda(text) for text in docs.reduced_body[:MAX_COUNT]])
accuracy_score(y_true, y_pred)
average_precision_score(y_true, y_pred)
print(classification_report(y_true, y_pred, target_names=mlb_t.classes_))
Out[42]:
0.027
Out[42]:
nan
precision recall f1-score support
.net 0.00 0.00 0.00 103
ajax 1.00 0.25 0.40 16
algorithm 0.58 0.21 0.31 33
amazon-web-services 0.00 0.00 0.00 0
android 1.00 0.08 0.14 26
android-layout 0.00 0.00 0.00 2
android-studio 0.00 0.00 0.00 3
angular 0.00 0.00 0.00 1
angularjs 1.00 0.17 0.29 6
animation 0.33 0.33 0.33 3
apache 0.00 0.00 0.00 3
apache-spark 0.00 0.00 0.00 1
apache-spark-sql 0.00 0.00 0.00 1
api 0.00 0.00 0.00 4
architecture 0.00 0.00 0.00 2
arrays 0.00 0.00 0.00 14
asp.net 0.00 0.00 0.00 33
asp.net-core 0.00 0.00 0.00 3
asp.net-mvc 0.00 0.00 0.00 14
asp.net-mvc-3 0.00 0.00 0.00 0
asp.net-mvc-4 0.00 0.00 0.00 1
asp.net-web-api 0.00 0.00 0.00 4
assembly 0.25 0.11 0.15 9
async-await 0.00 0.00 0.00 0
asynchronous 0.00 0.00 0.00 3
audio 0.00 0.00 0.00 3
authentication 1.00 0.14 0.25 7
bash 1.00 0.07 0.13 14
browser 0.00 0.00 0.00 3
build 0.50 0.17 0.25 6
c 0.00 0.00 0.00 49
c# 0.00 0.00 0.00 134
c++ 0.00 0.00 0.00 86
c++11 0.00 0.00 0.00 1
caching 0.00 0.00 0.00 5
class 0.25 0.12 0.17 8
cocoa 1.00 0.14 0.25 14
cocoa-touch 0.00 0.00 0.00 38
colors 0.00 0.00 0.00 0
command-line 0.00 0.00 0.00 7
concurrency 0.00 0.00 0.00 8
configuration 0.00 0.00 0.00 2
cookies 0.00 0.00 0.00 2
css 0.00 0.00 0.00 16
csv 0.60 0.75 0.67 4
data-structures 0.00 0.00 0.00 6
database 0.33 0.03 0.06 30
database-design 0.00 0.00 0.00 9
dataframe 0.00 0.00 0.00 3
date 1.00 0.50 0.67 8
datetime 1.00 0.10 0.18 10
debugging 0.00 0.00 0.00 16
deep-learning 0.00 0.00 0.00 2
dependency-injection 0.00 0.00 0.00 1
design-patterns 0.00 0.00 0.00 4
dictionary 1.00 0.25 0.40 4
django 1.00 0.46 0.63 13
docker 1.00 1.00 1.00 1
dom 0.00 0.00 0.00 3
eclipse 1.00 0.12 0.22 8
encryption 0.00 0.00 0.00 6
entity-framework 0.00 0.00 0.00 5
events 0.00 0.00 0.00 8
exception 0.50 0.20 0.29 5
express 0.00 0.00 0.00 1
facebook 0.00 0.00 0.00 2
file 0.22 0.36 0.28 11
firebase 0.00 0.00 0.00 1
flutter 0.00 0.00 0.00 2
forms 0.00 0.00 0.00 7
function 0.50 0.60 0.55 5
functional-programming 0.00 0.00 0.00 5
gcc 1.00 0.08 0.15 12
generics 0.00 0.00 0.00 7
git 1.00 0.08 0.14 13
github 0.50 1.00 0.67 1
google-chrome 0.00 0.00 0.00 4
gradle 0.50 0.33 0.40 3
hash 0.00 0.00 0.00 3
haskell 0.00 0.00 0.00 4
hibernate 1.00 0.38 0.55 8
html 1.00 0.08 0.15 37
http 0.20 0.17 0.18 12
https 0.00 0.00 0.00 7
image 0.44 0.70 0.54 10
image-processing 0.00 0.00 0.00 5
inheritance 0.00 0.00 0.00 6
installation 0.00 0.00 0.00 8
ios 0.00 0.00 0.00 67
ios7 0.00 0.00 0.00 19
ipad 0.00 0.00 0.00 2
iphone 1.00 0.02 0.03 64
jakarta-ee 0.00 0.00 0.00 7
java 1.00 0.08 0.16 106
java-8 0.00 0.00 0.00 1
javascript 0.00 0.00 0.00 86
jpa 0.00 0.00 0.00 5
jquery 1.00 0.23 0.37 48
json 1.00 0.33 0.50 12
keras 0.00 0.00 0.00 1
lambda 0.00 0.00 0.00 2
language-agnostic 0.00 0.00 0.00 17
laravel 0.00 0.00 0.00 2
linq 1.00 0.50 0.67 10
linux 1.00 0.14 0.25 35
list 0.38 0.43 0.40 7
logging 0.00 0.00 0.00 3
loops 0.00 0.00 0.00 3
machine-learning 0.00 0.00 0.00 5
macos 0.00 0.00 0.00 19
math 1.00 0.06 0.11 18
matplotlib 0.00 0.00 0.00 2
maven 0.00 0.00 0.00 2
memory 0.00 0.00 0.00 10
memory-management 0.00 0.00 0.00 6
mongodb 0.00 0.00 0.00 3
multithreading 0.00 0.00 0.00 25
mvvm 0.00 0.00 0.00 4
mysql 0.00 0.00 0.00 18
networking 0.00 0.00 0.00 2
neural-network 0.00 0.00 0.00 1
node.js 0.00 0.00 0.00 8
npm 0.00 0.00 0.00 2
numpy 1.00 0.25 0.40 4
object 0.12 0.33 0.17 6
objective-c 0.00 0.00 0.00 47
oop 0.33 0.08 0.13 12
opencv 0.00 0.00 0.00 3
optimization 0.50 0.09 0.15 11
orm 0.00 0.00 0.00 5
pandas 0.00 0.00 0.00 4
parsing 0.00 0.00 0.00 8
pdf 1.00 0.33 0.50 3
performance 0.50 0.03 0.05 36
php 1.00 0.03 0.06 34
pip 0.00 0.00 0.00 3
pointers 0.00 0.00 0.00 6
postgresql 0.00 0.00 0.00 6
python 0.75 0.03 0.06 92
python-2.7 0.00 0.00 0.00 3
python-3.x 0.00 0.00 0.00 6
r 0.00 0.00 0.00 5
reactjs 0.00 0.00 0.00 2
regex 1.00 0.33 0.50 3
rest 1.00 0.11 0.20 9
ruby 1.00 0.14 0.25 14
ruby-on-rails 0.00 0.00 0.00 16
ruby-on-rails-3 0.00 0.00 0.00 0
scala 0.00 0.00 0.00 1
scikit-learn 0.00 0.00 0.00 1
scipy 0.00 0.00 0.00 2
security 0.00 0.00 0.00 10
selenium 0.00 0.00 0.00 1
serialization 0.00 0.00 0.00 5
shell 1.00 0.10 0.18 20
sockets 0.00 0.00 0.00 5
sorting 1.00 0.10 0.18 10
spring 1.00 0.40 0.57 10
spring-boot 0.00 0.00 0.00 3
spring-mvc 0.00 0.00 0.00 6
sql 0.80 0.09 0.16 44
sql-server 0.00 0.00 0.00 39
sql-server-2008 0.00 0.00 0.00 9
sqlite 0.00 0.00 0.00 4
ssl 1.00 0.60 0.75 5
stl 0.00 0.00 0.00 8
string 0.56 0.50 0.53 18
swift 1.00 0.20 0.33 5
syntax 0.25 0.17 0.20 6
templates 0.00 0.00 0.00 5
tensorflow 0.00 0.00 0.00 2
terminal 0.00 0.00 0.00 3
testing 0.00 0.00 0.00 9
tomcat 0.50 0.17 0.25 6
tsql 0.00 0.00 0.00 21
twitter-bootstrap 0.00 0.00 0.00 8
types 0.00 0.00 0.00 6
typescript 0.00 0.00 0.00 1
ubuntu 0.00 0.00 0.00 2
uikit 0.00 0.00 0.00 7
uitableview 0.00 0.00 0.00 7
uiview 0.00 0.00 0.00 2
unicode 0.00 0.00 0.00 9
unit-testing 0.00 0.00 0.00 17
unix 0.00 0.00 0.00 14
url 0.40 0.50 0.44 8
user-interface 0.00 0.00 0.00 6
validation 1.00 0.20 0.33 5
variables 0.00 0.00 0.00 7
video 0.50 0.75 0.60 4
visual-studio 0.00 0.00 0.00 14
visual-studio-2010 0.00 0.00 0.00 1
web-services 0.00 0.00 0.00 13
windows 0.00 0.00 0.00 33
winforms 0.00 0.00 0.00 10
wpf 1.00 0.21 0.34 24
x86 0.00 0.00 0.00 9
xaml 1.00 0.11 0.20 9
xcode 0.50 0.20 0.29 10
xml 0.75 0.60 0.67 5
micro avg 0.58 0.07 0.12 2457
macro avg 0.25 0.09 0.11 2457
weighted avg 0.35 0.07 0.10 2457
samples avg 0.17 0.06 0.09 2457
In [43]:
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from sklearn.metrics import multilabel_confusion_matrix
matrix = multilabel_confusion_matrix(y_true, y_pred, samplewise=False)
for idx, label in enumerate(mlb_t.classes_):
print(f'{label} :\n TN:{matrix[idx][0,0]}\tFN:{matrix[idx][1,0]}\tTP:{matrix[idx][1,1]}\tFP:{matrix[idx][0,1]}')
from sklearn.metrics import multilabel_confusion_matrix
matrix = multilabel_confusion_matrix(y_true, y_pred, samplewise=False)
for idx, label in enumerate(mlb_t.classes_):
print(f'{label} :\n TN:{matrix[idx][0,0]}\tFN:{matrix[idx][1,0]}\tTP:{matrix[idx][1,1]}\tFP:{matrix[idx][0,1]}')
.net : TN:897 FN:103 TP:0 FP:0 ajax : TN:984 FN:12 TP:4 FP:0 algorithm : TN:962 FN:26 TP:7 FP:5 amazon-web-services : TN:1000 FN:0 TP:0 FP:0 android : TN:974 FN:24 TP:2 FP:0 android-layout : TN:998 FN:2 TP:0 FP:0 android-studio : TN:997 FN:3 TP:0 FP:0 angular : TN:997 FN:1 TP:0 FP:2 angularjs : TN:994 FN:5 TP:1 FP:0 animation : TN:995 FN:2 TP:1 FP:2 apache : TN:997 FN:3 TP:0 FP:0 apache-spark : TN:999 FN:1 TP:0 FP:0 apache-spark-sql : TN:999 FN:1 TP:0 FP:0 api : TN:995 FN:4 TP:0 FP:1 architecture : TN:997 FN:2 TP:0 FP:1 arrays : TN:986 FN:14 TP:0 FP:0 asp.net : TN:967 FN:33 TP:0 FP:0 asp.net-core : TN:997 FN:3 TP:0 FP:0 asp.net-mvc : TN:986 FN:14 TP:0 FP:0 asp.net-mvc-3 : TN:1000 FN:0 TP:0 FP:0 asp.net-mvc-4 : TN:999 FN:1 TP:0 FP:0 asp.net-web-api : TN:996 FN:4 TP:0 FP:0 assembly : TN:988 FN:8 TP:1 FP:3 async-await : TN:1000 FN:0 TP:0 FP:0 asynchronous : TN:996 FN:3 TP:0 FP:1 audio : TN:997 FN:3 TP:0 FP:0 authentication : TN:993 FN:6 TP:1 FP:0 bash : TN:986 FN:13 TP:1 FP:0 browser : TN:995 FN:3 TP:0 FP:2 build : TN:993 FN:5 TP:1 FP:1 c : TN:951 FN:49 TP:0 FP:0 c# : TN:866 FN:134 TP:0 FP:0 c++ : TN:914 FN:86 TP:0 FP:0 c++11 : TN:999 FN:1 TP:0 FP:0 caching : TN:995 FN:5 TP:0 FP:0 class : TN:989 FN:7 TP:1 FP:3 cocoa : TN:986 FN:12 TP:2 FP:0 cocoa-touch : TN:962 FN:38 TP:0 FP:0 colors : TN:1000 FN:0 TP:0 FP:0 command-line : TN:993 FN:7 TP:0 FP:0 concurrency : TN:991 FN:8 TP:0 FP:1 configuration : TN:998 FN:2 TP:0 FP:0 cookies : TN:998 FN:2 TP:0 FP:0 css : TN:984 FN:16 TP:0 FP:0 csv : TN:994 FN:1 TP:3 FP:2 data-structures : TN:994 FN:6 TP:0 FP:0 database : TN:968 FN:29 TP:1 FP:2 database-design : TN:991 FN:9 TP:0 FP:0 dataframe : TN:997 FN:3 TP:0 FP:0 date : TN:992 FN:4 TP:4 FP:0 datetime : TN:990 FN:9 TP:1 FP:0 debugging : TN:983 FN:16 TP:0 FP:1 deep-learning : TN:998 FN:2 TP:0 FP:0 dependency-injection : TN:999 FN:1 TP:0 FP:0 design-patterns : TN:996 FN:4 TP:0 FP:0 dictionary : TN:996 FN:3 TP:1 FP:0 django : TN:987 FN:7 TP:6 FP:0 docker : TN:999 FN:0 TP:1 FP:0 dom : TN:997 FN:3 TP:0 FP:0 eclipse : TN:992 FN:7 TP:1 FP:0 encryption : TN:994 FN:6 TP:0 FP:0 entity-framework : TN:995 FN:5 TP:0 FP:0 events : TN:992 FN:8 TP:0 FP:0 exception : TN:994 FN:4 TP:1 FP:1 express : TN:999 FN:1 TP:0 FP:0 facebook : TN:998 FN:2 TP:0 FP:0 file : TN:975 FN:7 TP:4 FP:14 firebase : TN:998 FN:1 TP:0 FP:1 flutter : TN:998 FN:2 TP:0 FP:0 forms : TN:993 FN:7 TP:0 FP:0 function : TN:992 FN:2 TP:3 FP:3 functional-programming : TN:995 FN:5 TP:0 FP:0 gcc : TN:988 FN:11 TP:1 FP:0 generics : TN:993 FN:7 TP:0 FP:0 git : TN:987 FN:12 TP:1 FP:0 github : TN:998 FN:0 TP:1 FP:1 google-chrome : TN:996 FN:4 TP:0 FP:0 gradle : TN:996 FN:2 TP:1 FP:1 hash : TN:997 FN:3 TP:0 FP:0 haskell : TN:996 FN:4 TP:0 FP:0 hibernate : TN:992 FN:5 TP:3 FP:0 html : TN:963 FN:34 TP:3 FP:0 http : TN:980 FN:10 TP:2 FP:8 https : TN:993 FN:7 TP:0 FP:0 image : TN:981 FN:3 TP:7 FP:9 image-processing : TN:995 FN:5 TP:0 FP:0 inheritance : TN:994 FN:6 TP:0 FP:0 installation : TN:992 FN:8 TP:0 FP:0 ios : TN:933 FN:67 TP:0 FP:0 ios7 : TN:981 FN:19 TP:0 FP:0 ipad : TN:998 FN:2 TP:0 FP:0 iphone : TN:936 FN:63 TP:1 FP:0 jakarta-ee : TN:993 FN:7 TP:0 FP:0 java : TN:894 FN:97 TP:9 FP:0 java-8 : TN:999 FN:1 TP:0 FP:0 javascript : TN:914 FN:86 TP:0 FP:0 jpa : TN:995 FN:5 TP:0 FP:0 jquery : TN:952 FN:37 TP:11 FP:0 json : TN:988 FN:8 TP:4 FP:0 keras : TN:999 FN:1 TP:0 FP:0 lambda : TN:997 FN:2 TP:0 FP:1 language-agnostic : TN:983 FN:17 TP:0 FP:0 laravel : TN:998 FN:2 TP:0 FP:0 linq : TN:990 FN:5 TP:5 FP:0 linux : TN:965 FN:30 TP:5 FP:0 list : TN:988 FN:4 TP:3 FP:5 logging : TN:997 FN:3 TP:0 FP:0 loops : TN:997 FN:3 TP:0 FP:0 machine-learning : TN:995 FN:5 TP:0 FP:0 macos : TN:981 FN:19 TP:0 FP:0 math : TN:982 FN:17 TP:1 FP:0 matplotlib : TN:998 FN:2 TP:0 FP:0 maven : TN:998 FN:2 TP:0 FP:0 memory : TN:987 FN:10 TP:0 FP:3 memory-management : TN:994 FN:6 TP:0 FP:0 mongodb : TN:997 FN:3 TP:0 FP:0 multithreading : TN:975 FN:25 TP:0 FP:0 mvvm : TN:996 FN:4 TP:0 FP:0 mysql : TN:982 FN:18 TP:0 FP:0 networking : TN:998 FN:2 TP:0 FP:0 neural-network : TN:999 FN:1 TP:0 FP:0 node.js : TN:992 FN:8 TP:0 FP:0 npm : TN:998 FN:2 TP:0 FP:0 numpy : TN:996 FN:3 TP:1 FP:0 object : TN:979 FN:4 TP:2 FP:15 objective-c : TN:953 FN:47 TP:0 FP:0 oop : TN:986 FN:11 TP:1 FP:2 opencv : TN:997 FN:3 TP:0 FP:0 optimization : TN:988 FN:10 TP:1 FP:1 orm : TN:995 FN:5 TP:0 FP:0 pandas : TN:996 FN:4 TP:0 FP:0 parsing : TN:992 FN:8 TP:0 FP:0 pdf : TN:997 FN:2 TP:1 FP:0 performance : TN:963 FN:35 TP:1 FP:1 php : TN:966 FN:33 TP:1 FP:0 pip : TN:997 FN:3 TP:0 FP:0 pointers : TN:994 FN:6 TP:0 FP:0 postgresql : TN:994 FN:6 TP:0 FP:0 python : TN:907 FN:89 TP:3 FP:1 python-2.7 : TN:997 FN:3 TP:0 FP:0 python-3.x : TN:994 FN:6 TP:0 FP:0 r : TN:995 FN:5 TP:0 FP:0 reactjs : TN:998 FN:2 TP:0 FP:0 regex : TN:997 FN:2 TP:1 FP:0 rest : TN:991 FN:8 TP:1 FP:0 ruby : TN:986 FN:12 TP:2 FP:0 ruby-on-rails : TN:984 FN:16 TP:0 FP:0 ruby-on-rails-3 : TN:1000 FN:0 TP:0 FP:0 scala : TN:999 FN:1 TP:0 FP:0 scikit-learn : TN:999 FN:1 TP:0 FP:0 scipy : TN:998 FN:2 TP:0 FP:0 security : TN:990 FN:10 TP:0 FP:0 selenium : TN:998 FN:1 TP:0 FP:1 serialization : TN:995 FN:5 TP:0 FP:0 shell : TN:980 FN:18 TP:2 FP:0 sockets : TN:995 FN:5 TP:0 FP:0 sorting : TN:990 FN:9 TP:1 FP:0 spring : TN:990 FN:6 TP:4 FP:0 spring-boot : TN:997 FN:3 TP:0 FP:0 spring-mvc : TN:994 FN:6 TP:0 FP:0 sql : TN:955 FN:40 TP:4 FP:1 sql-server : TN:961 FN:39 TP:0 FP:0 sql-server-2008 : TN:991 FN:9 TP:0 FP:0 sqlite : TN:996 FN:4 TP:0 FP:0 ssl : TN:995 FN:2 TP:3 FP:0 stl : TN:992 FN:8 TP:0 FP:0 string : TN:975 FN:9 TP:9 FP:7 swift : TN:995 FN:4 TP:1 FP:0 syntax : TN:991 FN:5 TP:1 FP:3 templates : TN:995 FN:5 TP:0 FP:0 tensorflow : TN:998 FN:2 TP:0 FP:0 terminal : TN:996 FN:3 TP:0 FP:1 testing : TN:989 FN:9 TP:0 FP:2 tomcat : TN:993 FN:5 TP:1 FP:1 tsql : TN:979 FN:21 TP:0 FP:0 twitter-bootstrap : TN:992 FN:8 TP:0 FP:0 types : TN:994 FN:6 TP:0 FP:0 typescript : TN:999 FN:1 TP:0 FP:0 ubuntu : TN:998 FN:2 TP:0 FP:0 uikit : TN:993 FN:7 TP:0 FP:0 uitableview : TN:993 FN:7 TP:0 FP:0 uiview : TN:998 FN:2 TP:0 FP:0 unicode : TN:991 FN:9 TP:0 FP:0 unit-testing : TN:983 FN:17 TP:0 FP:0 unix : TN:986 FN:14 TP:0 FP:0 url : TN:986 FN:4 TP:4 FP:6 user-interface : TN:994 FN:6 TP:0 FP:0 validation : TN:995 FN:4 TP:1 FP:0 variables : TN:993 FN:7 TP:0 FP:0 video : TN:993 FN:1 TP:3 FP:3 visual-studio : TN:986 FN:14 TP:0 FP:0 visual-studio-2010 : TN:999 FN:1 TP:0 FP:0 web-services : TN:987 FN:13 TP:0 FP:0 windows : TN:967 FN:33 TP:0 FP:0 winforms : TN:990 FN:10 TP:0 FP:0 wpf : TN:976 FN:19 TP:5 FP:0 x86 : TN:991 FN:9 TP:0 FP:0 xaml : TN:991 FN:8 TP:1 FP:0 xcode : TN:988 FN:8 TP:2 FP:2 xml : TN:994 FN:2 TP:3 FP:1
Supervised Classifier Model - WIP¶
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from sklearn.multioutput import MultiOutputClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(mlb_b.transform(docs.reduced_body), mlb_t.transform(docs.tags), test_size=0.4, random_state=0)
# TODO WIP
# clf = MultiOutputClassifier(KNeighborsClassifier(n_neighbors=10), n_jobs=-1).fit(X_train, y_train)
from sklearn.multioutput import MultiOutputClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(mlb_b.transform(docs.reduced_body), mlb_t.transform(docs.tags), test_size=0.4, random_state=0)
# TODO WIP
# clf = MultiOutputClassifier(KNeighborsClassifier(n_neighbors=10), n_jobs=-1).fit(X_train, y_train)
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# mlb_t.inverse_transform(clf.predict(X_test))
# mlb_t.inverse_transform(clf.predict(X_test))
Word2Vect¶
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# snippet taken from sample notebook
import gensim
import multiprocessing
from gensim.models import Word2Vec
# sentences = [ docs.split() for docs in docs.reduced_body]
sentences = [ docs.split() for docs in common_words]
print("Build & train Word2Vec model ...")
w2v_model = Word2Vec(sentences, min_count=1,vector_size=200, workers=multiprocessing.cpu_count())
# w2v_model.build_vocab(sentences)
# w2v_model.train(sentences, total_examples=1, epochs=1)
model_vectors = w2v_model.wv
w2v_words = model_vectors.index_to_key
print("Vocabulary size: %i" % len(w2v_words))
print("Word2Vec trained")
# snippet taken from sample notebook
import gensim
import multiprocessing
from gensim.models import Word2Vec
# sentences = [ docs.split() for docs in docs.reduced_body]
sentences = [ docs.split() for docs in common_words]
print("Build & train Word2Vec model ...")
w2v_model = Word2Vec(sentences, min_count=1,vector_size=200, workers=multiprocessing.cpu_count())
# w2v_model.build_vocab(sentences)
# w2v_model.train(sentences, total_examples=1, epochs=1)
model_vectors = w2v_model.wv
w2v_words = model_vectors.index_to_key
print("Vocabulary size: %i" % len(w2v_words))
print("Word2Vec trained")
Build & train Word2Vec model ... Vocabulary size: 1102 Word2Vec trained
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# training another model, will give slightly different results
model = Word2Vec(sentences, min_count=1, vector_size=200, workers=multiprocessing.cpu_count())
# model.build_vocab(sentences)
# model.train(sentences, total_examples=1, epochs=1)
# model.wv.index_to_key
print("Vocabulary size: %i" % len(model.wv.index_to_key))
# training another model, will give slightly different results
model = Word2Vec(sentences, min_count=1, vector_size=200, workers=multiprocessing.cpu_count())
# model.build_vocab(sentences)
# model.train(sentences, total_examples=1, epochs=1)
# model.wv.index_to_key
print("Vocabulary size: %i" % len(model.wv.index_to_key))
Vocabulary size: 1102
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# guess close words
len(model.wv.index_to_key)
guess = 'java'
model.wv[guess]
model.wv.most_similar(guess)
# w2v_model.wv[guess]
w2v_model.wv.most_similar(guess)
# guess close words
len(model.wv.index_to_key)
guess = 'java'
model.wv[guess]
model.wv.most_similar(guess)
# w2v_model.wv[guess]
w2v_model.wv.most_similar(guess)
Out[48]:
1102
Out[48]:
array([-8.0685975e-04, 1.8478620e-03, -3.5784244e-05, 7.8556535e-04,
-7.0988300e-04, 2.5089872e-03, 3.3027362e-03, -2.2366261e-03,
-1.0397923e-03, 3.0224586e-03, -3.1244112e-03, 4.1021919e-03,
-2.8160298e-03, 3.9719819e-04, 3.3212495e-03, -2.3031449e-03,
2.3565544e-03, 3.4527206e-03, 1.0563791e-03, 1.2479031e-03,
-3.6957038e-03, 3.6313105e-03, 1.2979353e-03, -7.8089000e-04,
5.8797596e-04, 1.6136229e-03, 2.6066375e-03, -4.6743155e-03,
-2.6989235e-03, -2.4546587e-03, -4.8870398e-03, -1.9378293e-03,
2.7867174e-03, 4.2256033e-03, 9.9620223e-04, 1.8676221e-03,
1.8274784e-05, -4.5388732e-03, 9.8327400e-05, 5.8128120e-04,
4.8478115e-03, -6.5114500e-04, 4.7930242e-03, 2.6198984e-03,
4.1404511e-03, -2.8644563e-03, -4.4114627e-03, -1.6621852e-03,
-2.9153479e-03, -3.2962488e-03, -3.5746873e-03, 2.6904417e-03,
1.8659878e-03, -5.3888676e-04, -3.6092340e-03, -2.8309692e-03,
2.8791977e-03, -6.6956994e-04, -4.9129603e-03, -1.6979217e-04,
-3.3589303e-03, 3.8888692e-03, 4.0155530e-04, -2.5636733e-03,
-4.8187315e-03, -4.3193209e-03, -1.5251279e-04, -2.5193167e-03,
3.9290879e-03, -4.4381227e-03, -4.8505343e-03, -4.5778682e-03,
2.2227527e-03, -1.5687824e-03, 1.7094243e-03, 2.3609411e-03,
1.5264392e-03, 2.6576924e-03, -3.6822225e-03, 3.8068963e-03,
-3.9939308e-03, 3.3648289e-03, -8.5964683e-04, 3.9901864e-03,
4.9415934e-03, -3.0475557e-03, -1.1421073e-03, -3.1999040e-03,
1.4359462e-03, -4.8017013e-03, -2.0715070e-03, -9.2119572e-04,
-1.7586541e-03, -2.8540491e-04, 3.2883763e-04, -3.2718515e-03,
3.4779417e-03, 4.7514904e-03, 4.7108103e-03, -1.1618590e-03,
3.0511379e-04, -1.1638284e-03, -4.7379732e-04, -1.0691583e-03,
1.0196590e-03, -3.3270549e-03, 3.5882234e-03, 9.2787383e-04,
-4.0828516e-03, -3.8226962e-03, 1.2723124e-03, 4.4728946e-03,
2.1326637e-03, -2.7278901e-04, -4.1147005e-03, 9.4922900e-04,
-2.0066500e-04, -1.4082253e-03, 2.8750633e-03, -1.6428471e-03,
4.7505903e-03, 4.8528314e-03, 4.4565927e-03, -3.8812207e-03,
-4.0463982e-03, -3.6187374e-03, -3.5457099e-03, 3.0076384e-04,
-1.3258052e-03, -3.0814898e-03, -3.4515429e-03, -4.9340832e-03,
-4.1334974e-03, 1.1620044e-03, 4.0553715e-03, -4.9678180e-03,
-3.1405843e-03, 1.3334024e-03, -4.6967147e-03, -1.9301128e-03,
-2.8312076e-03, 1.1620451e-03, 6.6246750e-04, 7.7108620e-04,
-1.1299241e-03, 4.2644083e-03, 4.4806134e-03, -2.5460660e-03,
-2.3049773e-03, 4.2400886e-03, -1.3010514e-03, 3.3850074e-04,
3.3672689e-03, -4.9998425e-03, -1.3753772e-03, -1.0140813e-03,
-4.8190952e-04, -4.4220672e-03, 2.8061878e-03, 1.9601821e-03,
-3.9038789e-03, 2.1441912e-03, 2.7279162e-03, -1.9918573e-03,
4.6905042e-03, -2.6115871e-03, 5.7787419e-04, -6.5438985e-04,
-3.2176971e-04, 1.0849965e-03, -1.4097786e-03, -3.6913336e-03,
4.0633236e-03, -2.5698447e-03, -3.2793963e-03, 3.4035647e-03,
-3.3517003e-03, -3.5246612e-05, 2.0492577e-03, -2.0808256e-03,
-5.8817270e-04, -4.7293198e-03, -4.6870471e-03, -2.4540913e-03,
-3.9687143e-03, 1.3483488e-03, -3.7505626e-04, -3.0024804e-03,
-1.0738946e-03, 4.8253606e-03, -4.0750680e-03, 3.3054519e-03,
-2.5669157e-03, -1.8279159e-03, 2.0645999e-03, 3.1377317e-04,
1.6976643e-03, -1.3787901e-03, 3.9975559e-03, -1.3283384e-03],
dtype=float32)
Out[48]:
[('advantage', 0.23929814994335175),
('sql-server', 0.19636934995651245),
('task', 0.19634023308753967),
('prevent', 0.18997704982757568),
('std', 0.18560335040092468),
('doc', 0.17318908870220184),
('io', 0.17265936732292175),
('driver', 0.16542349755764008),
('caching', 0.16399025917053223),
('put', 0.16215522587299347)]
Out[48]:
[('advantage', 0.23929814994335175),
('sql-server', 0.19636934995651245),
('task', 0.19634023308753967),
('prevent', 0.18997704982757568),
('std', 0.18560335040092468),
('doc', 0.17318908870220184),
('io', 0.17265936732292175),
('driver', 0.16542349755764008),
('caching', 0.16399025917053223),
('put', 0.16215522587299347)]
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with open('w2v_model', 'wb') as v :
# pickle.dump(w2v_model, v)
w2v_model.save(v)
with open('w2v_model', 'wb') as v :
# pickle.dump(w2v_model, v)
w2v_model.save(v)
Averaged vector for documents¶
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# compute average vector for each document
averaged_docs = [np.mean([w2v_model.wv[word] for word in doc.split()], axis=0) for doc in docs.reduced_body ]
# averaged_docs
docs['wv_averaged_doc'] = averaged_docs
averaged_docs
# compute average vector for each document
averaged_docs = [np.mean([w2v_model.wv[word] for word in doc.split()], axis=0) for doc in docs.reduced_body ]
# averaged_docs
docs['wv_averaged_doc'] = averaged_docs
averaged_docs
Out[50]:
[array([-2.42229187e-04, -2.14630301e-04, -2.28896111e-04, -2.43320566e-04,
-1.24798622e-04, -5.59168810e-04, 2.45753530e-04, 1.62574739e-04,
-3.04421759e-04, 5.35650470e-04, 7.51194675e-05, -7.73447158e-04,
-8.12313869e-04, -2.97249411e-04, -2.85423768e-04, -9.73651140e-07,
4.29962938e-06, -1.42363977e-04, 6.54013944e-04, -3.32716700e-05,
-4.83660478e-05, -1.88423612e-04, -5.33699349e-04, -5.72375604e-04,
4.08686756e-04, -6.13061129e-04, 4.03989659e-04, 2.47826421e-04,
3.74296797e-04, -2.27315250e-04, 9.98115167e-04, 8.49255521e-05,
-2.88651463e-05, -6.32138981e-04, -4.81609241e-05, 3.83325969e-04,
-1.56378912e-04, -2.30191818e-05, 1.85409139e-04, 1.19736978e-04,
-8.12911545e-04, 1.98158901e-04, -1.36887960e-04, 5.53273654e-04,
-7.81657582e-05, -1.49639978e-04, 4.73961758e-04, 1.15257368e-04,
-1.33751630e-04, -2.97145616e-05, -1.35826340e-04, 5.66167073e-05,
5.84215624e-04, 1.81630181e-04, 5.59981738e-04, 5.04146097e-04,
6.04829926e-04, -4.19069227e-04, 9.15113604e-04, -9.76773881e-05,
3.38501326e-04, -8.23782902e-05, -3.68395675e-04, 5.50582990e-05,
-9.03413340e-04, -2.67181545e-04, -8.56360479e-04, 1.91513842e-04,
8.56790430e-05, 1.09259345e-04, 6.52840012e-04, 2.22366332e-04,
3.61557992e-04, -2.47116637e-04, -1.81769617e-04, -5.54343060e-05,
-4.07341548e-04, 6.87739812e-05, 6.43700245e-04, 1.31915836e-03,
-1.40971344e-04, -4.20853205e-04, -1.46557213e-04, 1.07686890e-06,
-3.85275227e-04, 1.66253958e-04, -2.45759002e-04, 4.62087628e-04,
5.09190704e-06, 1.94169028e-04, -4.93726751e-04, -7.88856414e-04,
-6.61803278e-05, 6.96943025e-04, 2.58993299e-04, -5.72842764e-05,
1.28009182e-04, -1.17225558e-04, 4.83801123e-05, 4.31624416e-04,
-6.10032177e-04, 5.67555253e-04, -2.00145194e-04, -5.72663557e-04,
4.85927128e-04, 5.93341712e-04, -1.40998032e-04, 3.36601748e-04,
-3.71296308e-04, 4.58863738e-04, 1.30674802e-03, 5.32702368e-04,
-7.62841155e-05, -8.69454117e-04, -5.84699272e-04, -5.12204715e-04,
2.48034543e-04, 3.68101901e-04, 4.96283101e-05, 3.57201643e-04,
7.32743822e-04, -4.69218037e-04, 1.00257643e-03, -9.53875933e-05,
3.98891221e-04, 7.03429221e-04, -2.69054755e-04, -1.39575233e-04,
4.01889964e-04, 5.52340935e-04, -6.80440062e-05, 1.11374502e-04,
8.18581830e-05, -2.23779557e-06, 4.59269126e-04, -8.17324617e-04,
4.11064189e-04, -2.91694505e-06, -1.87106882e-04, 7.06683495e-04,
2.07362609e-04, 5.38938155e-04, -1.99831062e-04, 2.67581170e-04,
7.13098503e-04, -2.01555624e-04, 4.68435028e-04, -3.31444928e-04,
2.36872525e-04, 1.09682861e-03, -2.75436178e-04, 3.58935300e-04,
-2.93325520e-05, -2.81787186e-04, 5.74333710e-04, 2.69448734e-04,
6.64162741e-04, -5.32879843e-04, -5.43489004e-04, -6.15847704e-04,
2.41907474e-05, 4.63486125e-04, 8.68271978e-04, 4.17056057e-04,
-1.08456792e-04, -2.56572675e-04, 2.44821946e-04, -9.67476008e-05,
-1.95452070e-04, -7.95498127e-05, -1.05239400e-04, 2.82974914e-04,
-2.35537991e-05, -3.97636264e-04, 6.89635170e-04, -5.41031361e-04,
-2.30518010e-04, 1.26839441e-04, -1.86083867e-04, -1.24559607e-04,
-2.84049776e-04, -4.61303898e-05, -8.01845817e-05, 4.63638244e-05,
8.79003434e-04, 1.20360171e-04, -1.82682692e-04, -9.21034080e-05,
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3.59017613e-05, -9.36565339e-05, -4.64092795e-04, -3.40657803e-04,
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3.23294982e-04, 4.75561130e-04, -4.71406347e-05, 3.18403560e-04,
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2.44595140e-05, 3.82504775e-04, 6.78190321e-04, 2.67752301e-04,
4.02357400e-04, -8.22544331e-04, 1.59490839e-04, -7.45400248e-05,
5.89580595e-05, 4.88040736e-04, 6.69993024e-05, 1.33078036e-04,
5.68735122e-04, 7.01090030e-04, -2.23755516e-04, -4.64715697e-07,
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2.20439964e-04, 3.19271669e-04, 3.10951786e-04, -8.93142831e-04,
5.65248658e-04, 2.72382254e-04, 6.32745156e-04, 6.91298803e-04,
1.03592574e-05, 6.57655983e-05, -3.60931474e-04, -7.72285624e-04,
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2.69499898e-04, -4.53189277e-04, 4.60370589e-04, 1.95099652e-04,
-9.77594827e-05, -9.75372532e-05, -4.52223554e-04, 4.01235389e-04,
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2.65428844e-06, 3.38919548e-04, 4.82909272e-05, -1.46655744e-04,
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-3.64246109e-04, -1.77700174e-04, 6.56261473e-05, -1.30284764e-03],
dtype=float32),
array([ 6.64010935e-04, 2.10495447e-04, 4.52899403e-04, 1.84546865e-04,
5.65950351e-04, 1.02320232e-03, 3.75639764e-04, 9.04292567e-04,
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1.24272599e-04, -4.14765294e-04, 1.53475243e-03, 7.03744066e-04,
4.89410653e-04, -6.33444113e-04, 8.10613928e-05, 4.26314800e-04,
2.63709779e-04, 7.29064122e-05, -7.91258877e-04, -1.66711441e-04,
7.53890723e-04, -3.49596987e-04, 2.81170214e-04, 3.53390787e-04,
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3.15416808e-04, 1.53190093e-04, 2.91225151e-04, 1.32336514e-03,
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2.04829033e-04, -3.24457564e-04, 1.91972766e-04, -9.21200786e-04,
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4.97576148e-05, -1.14279947e-05, 9.31144241e-05, -8.85680958e-04],
dtype=float32),
array([-7.67248450e-04, 2.73501646e-04, 9.91316047e-04, -2.92345416e-04,
4.38281597e-04, 8.90112191e-04, -2.04106007e-04, 5.73027122e-04,
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6.76924479e-04, 2.40114765e-04, -5.23331109e-04, 2.00326991e-04,
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3.43048072e-04, -3.41148232e-04, -3.86819040e-04, -6.43728359e-04],
dtype=float32),
...]
Clusterize embeded documents¶
In [51]:
Copied!
from sklearn.cluster import KMeans
kmeans = KMeans(n_clusters=200, random_state=0).fit(averaged_docs)
from sklearn.cluster import KMeans
kmeans = KMeans(n_clusters=200, random_state=0).fit(averaged_docs)
In [52]:
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len(averaged_docs)
len(averaged_docs[0])
kmeans.labels_
kmeans.labels_.min()
kmeans.labels_.max()
len(averaged_docs)
len(averaged_docs[0])
kmeans.labels_
kmeans.labels_.min()
kmeans.labels_.max()
Out[52]:
27128
Out[52]:
200
Out[52]:
array([ 28, 179, 54, ..., 154, 25, 33], dtype=int32)
Out[52]:
0
Out[52]:
199
In [53]:
Copied!
kmeans.cluster_centers_[0]
kmeans.cluster_centers_[0]
Out[53]:
array([ 1.87103171e-04, -4.60651989e-05, 6.58375640e-05, 9.54433839e-05,
-5.44459608e-04, -4.41727980e-04, -2.23092234e-04, 9.40112425e-04,
-2.51741801e-05, 1.08755143e-04, 1.35601210e-04, -1.14671180e-04,
-7.99671967e-04, 7.19306139e-04, -3.58160678e-04, -4.65666665e-04,
7.55818572e-04, -1.09480728e-03, 2.93239497e-04, 3.50788552e-05,
5.47110195e-04, 4.28829598e-04, -6.22290863e-04, -3.08010933e-04,
-3.57548543e-04, -1.02269904e-03, 5.47532388e-04, 1.81620484e-04,
3.91602951e-04, -2.44864094e-05, 9.91097279e-04, 1.13202738e-04,
-5.79886213e-04, 4.24187426e-04, -4.96919006e-04, 6.42460929e-04,
-6.10722917e-05, -3.09525274e-04, -8.00877034e-04, 4.40787074e-04,
7.58504603e-04, -1.38402534e-04, -5.75077444e-04, -1.16501435e-04,
-1.10370880e-04, -1.04804348e-03, 7.07079486e-04, -6.68176902e-04,
6.76199492e-04, -5.48457991e-05, 4.07728077e-04, 3.85062413e-05,
6.57923514e-04, 5.51215833e-06, 5.43943946e-05, 7.68777585e-04,
7.48259200e-04, -4.21222987e-04, 7.10349625e-04, -8.99817766e-04,
-3.21533320e-04, 1.94239739e-04, 2.88595822e-04, -2.17431278e-04,
4.54164984e-05, -8.11110479e-05, -4.91757671e-04, 5.85476705e-05,
1.81312488e-04, 3.09509618e-04, -1.57842836e-04, 1.28186548e-04,
-5.59939620e-04, 6.62758925e-04, 5.18639335e-04, -6.72123818e-04,
2.18009618e-04, -3.63018018e-04, 6.61748500e-05, 1.87534873e-04,
7.81709133e-05, 3.47499809e-04, -2.51448061e-04, 4.87469159e-04,
4.51611312e-04, -5.82718116e-04, -1.14931145e-04, 9.92233334e-05,
5.27636157e-04, 7.83160439e-04, -6.05411931e-04, -1.86506975e-04,
2.38625789e-04, -6.90644813e-04, 1.08760017e-04, 1.78585983e-04,
-1.32225052e-04, -2.93382455e-04, 3.39541035e-04, 5.27347930e-04,
2.66533306e-04, 9.20520666e-04, 2.44690201e-04, 4.00639736e-04,
-2.81330837e-04, 5.84947493e-04, -3.16678193e-04, 5.82150280e-04,
7.16184579e-04, -5.95973653e-04, 1.43605501e-04, -1.11350662e-04,
4.87121500e-04, -1.26404532e-05, -4.41578123e-04, 1.87610156e-04,
1.80540092e-04, -1.60109489e-05, -1.81112313e-04, 4.73745444e-04,
-4.28233633e-04, -4.48495580e-04, 2.40678835e-04, 2.67935295e-04,
-1.36126691e-04, -9.39143698e-05, -4.39178073e-04, -2.69410276e-04,
2.89523263e-04, -1.71077428e-04, 1.63892868e-04, -3.56913593e-04,
2.81843261e-04, -2.08125883e-04, -2.34156828e-05, 6.07307553e-04,
-3.00858774e-04, -9.98787557e-04, 8.57288440e-04, -9.05139378e-06,
-3.77640053e-04, 3.07789020e-04, -1.07990067e-03, -9.36099600e-05,
4.01883307e-04, 3.95574384e-04, 6.63852887e-04, 3.91781371e-04,
-8.80625509e-04, 6.63307370e-04, -2.78991473e-04, 6.44455487e-04,
8.56982587e-05, 6.01673235e-04, -5.10122264e-04, 6.93614041e-04,
1.00309547e-04, 4.27301655e-04, 2.84584767e-04, -5.20849429e-05,
-2.18326079e-04, 3.51667258e-04, -8.57506036e-05, -1.03292505e-04,
-5.02365852e-04, -5.18552185e-05, -3.51718796e-04, 1.89932947e-05,
-7.76193360e-05, 7.84101498e-04, -8.03868325e-04, 3.27087139e-04,
-6.87935686e-04, -4.88693721e-04, 6.08733859e-04, 4.73016857e-04,
-4.12523178e-06, -8.12167157e-05, 2.39839779e-04, -5.38041089e-04,
3.51941758e-04, 5.46808308e-04, 6.66733492e-04, 1.57444268e-04,
-3.52755200e-04, -7.34084405e-04, -7.37202202e-04, -7.24928133e-04,
-4.82666000e-04, -7.55196804e-05, -3.82446946e-04, 6.71289073e-04,
4.33777719e-04, 2.10591037e-04, 1.03327810e-03, -3.21777366e-04,
1.54683032e-04, 8.72502938e-05, -8.05033132e-04, -4.81991919e-04])
In [54]:
Copied!
print("Most representative terms per cluster (based on centroids):")
for i in range(50):
tokens_per_cluster = ""
most_representative = model.wv.most_similar(positive=[kmeans.cluster_centers_[i]], topn=5)
for t in most_representative:
tokens_per_cluster += f"{t[0]} "
print(f"Cluster {i}: {tokens_per_cluster}")
print("Most representative terms per cluster (based on centroids):")
for i in range(50):
tokens_per_cluster = ""
most_representative = model.wv.most_similar(positive=[kmeans.cluster_centers_[i]], topn=5)
for t in most_representative:
tokens_per_cluster += f"{t[0]} "
print(f"Cluster {i}: {tokens_per_cluster}")
Most representative terms per cluster (based on centroids): Cluster 0: email using sending nt creating Cluster 1: install gem solve class response Cluster 2: url provide remove seen memory-management Cluster 3: code single following datetime bug Cluster 4: algorithm scrolling title library specific Cluster 5: kernel random language copy module Cluster 6: example prevent method safe following Cluster 7: certificate programming using profile scala Cluster 8: point two socket simply use Cluster 9: password thinking specific seen name Cluster 10: background right parsing structure set Cluster 11: directory file actually find express Cluster 12: object access pdf tomcat confused Cluster 13: hash ipad create key function Cluster 14: thread separate guess practice problem Cluster 15: difference decided object particular called Cluster 16: image android valid bug path Cluster 17: python use regular result style Cluster 18: view nt obvious obviously oop Cluster 19: class helpful behavior context bug Cluster 20: jquery plugin show use simple Cluster 21: element using statement instead output Cluster 22: json object like way pattern Cluster 23: default constructor bug git via Cluster 24: error basic domain linq access Cluster 25: function argument definition modify scope Cluster 26: list also data-structures yet location Cluster 27: application math thought web mysql Cluster 28: nt know anyone ca dependency Cluster 29: would like make help library Cluster 30: server client like however mac Cluster 31: browser db later ajax working Cluster 32: use know guide program one Cluster 33: number using together linq large Cluster 34: service using active date network Cluster 35: class context bind bug activity Cluster 36: difference decided object program condition Cluster 37: package dependency failed safe note Cluster 38: memory thanks access icon scrolling Cluster 39: regex end sql-server animation mvc Cluster 40: string default want sequence regular Cluster 41: module moment character answer guess Cluster 42: achieve mind job templates since Cluster 43: script run need followed print Cluster 44: item mention session step perform Cluster 45: value expect especially reference current Cluster 46: convert specified java configure template Cluster 47: process bind try schema came Cluster 48: static compiler choice delete main Cluster 49: add row view obvious want
In [55]:
Copied!
# TODO classify
# faire une reduction de dimension PCA ?
sentence = ["topic", "python"]
# sentence_vector = np.mean([w2v_model.wv[w2v_model.wv.most_similar(word)[0][0]] for word in sentence], axis=0)
sentence_vector = np.mean([w2v_model.wv[word] for word in sentence], axis=0)
sentence_vector
# TODO classify
# faire une reduction de dimension PCA ?
sentence = ["topic", "python"]
# sentence_vector = np.mean([w2v_model.wv[w2v_model.wv.most_similar(word)[0][0]] for word in sentence], axis=0)
sentence_vector = np.mean([w2v_model.wv[word] for word in sentence], axis=0)
sentence_vector
Out[55]:
array([-4.0601883e-03, -3.9267479e-04, -1.4340163e-03, -2.1644880e-03,
-1.5921283e-03, -2.6663046e-03, -4.9456477e-04, 4.6618632e-04,
-5.8609177e-05, 4.1464210e-04, -2.2711558e-03, 3.5677722e-04,
5.5753230e-04, -1.4130294e-03, 1.8964612e-03, 8.8387192e-04,
-3.3596617e-03, 2.9975187e-03, 6.8198156e-04, 1.4043075e-03,
5.9972634e-04, -3.3933697e-03, 3.9709061e-03, -9.6261443e-05,
-1.2507963e-03, -2.0137494e-03, 1.3280767e-03, 2.0886015e-03,
-4.7273154e-04, -3.7963521e-03, 5.7390635e-04, 4.1547334e-03,
9.6712832e-04, 6.8186520e-04, 2.0640576e-03, -3.8947159e-04,
-3.6245906e-03, -5.7032937e-04, 1.8354654e-03, -1.1683791e-03,
-2.2637576e-03, 2.1834099e-03, 1.2037170e-03, -1.8130947e-03,
1.2560117e-03, -2.4885917e-03, -8.1596919e-04, -1.7682778e-03,
-1.4140636e-03, -1.0895777e-03, 3.7537045e-03, -3.6345777e-04,
9.1437571e-04, 7.8678608e-04, -3.5949051e-03, -6.7801471e-04,
-4.7733122e-03, -3.8288331e-03, -8.4282761e-04, -1.0762382e-03,
3.5781520e-03, 2.9419046e-03, -1.4690763e-03, 5.4585340e-04,
3.3448981e-03, -2.8345501e-03, -2.2163868e-03, -2.3640154e-03,
-2.1164436e-03, 1.9592959e-03, -8.7685941e-04, 3.0684297e-04,
2.9095178e-03, 2.8524529e-03, 3.6589943e-03, 8.9173554e-04,
3.6091087e-04, 2.3707838e-03, -4.1119516e-04, -9.5140282e-04,
4.0829694e-03, -1.5166985e-03, 1.0809421e-03, -3.1212640e-03,
-7.6650560e-04, -1.7369717e-03, 2.0799702e-03, -3.1045640e-03,
1.2816871e-03, -1.3467250e-04, 1.4156103e-06, 2.1564900e-03,
4.5037363e-05, 8.4786414e-05, -3.0229157e-03, 3.3288298e-04,
4.2328071e-03, 4.4922759e-03, -1.6439497e-04, -1.2080771e-03,
2.4424552e-04, -1.2494295e-03, -3.3044512e-04, 1.9422150e-03,
4.2388299e-03, 1.7558155e-04, -1.9336934e-05, 1.5868247e-03,
-1.8238622e-03, -1.3775814e-03, 1.2391323e-03, -3.5424673e-03,
-3.0497010e-03, -1.3675790e-03, 1.4043415e-03, -2.7432514e-03,
9.1426435e-04, 1.8978990e-03, 3.3784974e-03, -9.8470505e-04,
3.1252664e-03, -4.7334163e-03, -2.8021233e-03, 2.2161645e-03,
-8.0626726e-04, 1.5482843e-04, 1.3085610e-03, -4.3652713e-04,
-2.3331130e-03, -7.7721011e-04, -4.4600056e-03, -8.0977264e-04,
5.4656330e-04, 3.9510122e-03, 1.1527360e-03, 2.3601698e-03,
3.8664404e-04, -2.1505230e-03, 5.4119818e-04, -3.2785684e-03,
-5.4717896e-04, 2.5376077e-03, -2.3902436e-03, -3.2515549e-03,
-4.7724182e-04, -8.1264973e-04, -2.3491303e-03, -8.4324536e-04,
-4.2857591e-04, 1.8232447e-03, -1.2847441e-03, 3.0791946e-03,
-1.4363277e-03, 3.4051895e-04, 3.6716340e-03, 1.7638088e-03,
-1.3040067e-03, -1.1667347e-03, 1.5788168e-03, 2.4008262e-03,
6.9153315e-04, 1.0807222e-03, -7.7513757e-04, 2.8717124e-03,
7.1114965e-04, 1.4373250e-03, 6.4289325e-04, 4.1040243e-03,
-2.4891847e-03, -4.4930342e-04, 1.0521471e-03, -8.8419439e-04,
-2.2580654e-03, 1.3895923e-03, -2.5053150e-03, -3.0954712e-04,
-1.2562001e-03, -2.6186327e-03, -1.5562630e-03, -7.1350695e-04,
2.9190897e-03, -1.9724357e-03, -9.7601896e-04, -1.3462144e-03,
1.9928713e-03, 1.7370486e-03, -3.1984388e-03, 4.1313805e-03,
-1.1455535e-03, -1.5752226e-03, 4.6912729e-04, -1.5479920e-04,
-1.0863233e-03, 2.8889705e-03, -1.4043474e-03, -9.0638467e-04,
-2.5167519e-03, -5.9622171e-04, 2.4435436e-03, 2.9504746e-03],
dtype=float32)
In [56]:
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# similar = np.mean([w2v_model.wv.most_similar(word) for word in sentence.split()], axis=0)
cluster = kmeans.transform([sentence_vector])
# similar = np.mean([w2v_model.wv.most_similar(word) for word in sentence.split()], axis=0)
cluster = kmeans.transform([sentence_vector])
In [57]:
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cluster
cluster
Out[57]:
array([[0.03116307, 0.02998555, 0.02957759, 0.03023573, 0.03107989,
0.03009886, 0.02991852, 0.03031623, 0.03063395, 0.02980115,
0.03041027, 0.03009612, 0.03037171, 0.03040231, 0.02997538,
0.03428981, 0.02978448, 0.02146342, 0.02994282, 0.03120026,
0.03471273, 0.03028633, 0.02971482, 0.03019099, 0.0307026 ,
0.02987595, 0.03004159, 0.03083936, 0.02979704, 0.02979062,
0.02984581, 0.03000894, 0.02965344, 0.03086803, 0.03076673,
0.02984034, 0.03088391, 0.03063985, 0.03018502, 0.05255384,
0.03033248, 0.03029784, 0.05279628, 0.02983271, 0.03037851,
0.03094432, 0.04669883, 0.03082345, 0.05091422, 0.03090063,
0.03077665, 0.03073141, 0.02970645, 0.03053867, 0.03087294,
0.03101767, 0.03084085, 0.02958568, 0.03085245, 0.03012601,
0.02971983, 0.03023171, 0.0322118 , 0.02963651, 0.03239077,
0.03027016, 0.02981664, 0.03410695, 0.0305607 , 0.02988142,
0.03422679, 0.03023482, 0.02994723, 0.03005418, 0.03061616,
0.0500766 , 0.0294323 , 0.03072859, 0.0299998 , 0.02990345,
0.03064963, 0.03059443, 0.03035119, 0.02970593, 0.03432977,
0.030283 , 0.02977711, 0.03059136, 0.03201091, 0.02976047,
0.03045232, 0.03900582, 0.03005997, 0.03095231, 0.0299444 ,
0.05079335, 0.02983797, 0.03041416, 0.03011977, 0.03021546,
0.03043672, 0.04790086, 0.0302167 , 0.03052694, 0.03165567,
0.03051125, 0.03005015, 0.04133063, 0.03103294, 0.02983456,
0.03303473, 0.03015202, 0.03136955, 0.03046743, 0.02984212,
0.03014044, 0.04066319, 0.03111978, 0.03007004, 0.02997428,
0.03134155, 0.03088373, 0.04152232, 0.03054319, 0.03086662,
0.03005352, 0.0320032 , 0.030026 , 0.03012462, 0.02995639,
0.0296372 , 0.03101386, 0.03062815, 0.02986232, 0.03004889,
0.03006075, 0.03177359, 0.03002278, 0.03199002, 0.03156837,
0.03045365, 0.02990428, 0.03072163, 0.03703212, 0.02985528,
0.04529487, 0.03016059, 0.03109522, 0.02990036, 0.03177059,
0.03104806, 0.03229741, 0.03176434, 0.0370829 , 0.03012994,
0.03017949, 0.02951919, 0.02891987, 0.03085368, 0.02981542,
0.03308918, 0.03025362, 0.03029043, 0.02435378, 0.0272149 ,
0.03358341, 0.03361475, 0.03648482, 0.02987151, 0.02990268,
0.02999253, 0.03050823, 0.03035029, 0.03009441, 0.04078333,
0.03481598, 0.02971536, 0.04649077, 0.03077686, 0.03017952,
0.03082485, 0.03023487, 0.02970606, 0.03112428, 0.03775937,
0.03007784, 0.03000527, 0.03046755, 0.03038067, 0.0314219 ,
0.03551175, 0.02954681, 0.03439649, 0.03023886, 0.02986399,
0.03020117, 0.03017424, 0.03025351, 0.02982996, 0.03055704]])
In [62]:
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model.wv.most_similar(cluster)
model.wv.most_similar(cluster)
Out[62]:
[('uikit', 0.2586398720741272),
('used', 0.22127950191497803),
('choice', 0.21761272847652435),
('loops', 0.20685872435569763),
('mac', 0.1982603818178177),
('console', 0.19101263582706451),
('permission', 0.18170540034770966),
('cell', 0.1810801476240158),
('wanted', 0.17971324920654297),
('writing', 0.17652106285095215)]
Appendix¶
à lire
topic modelling : unsupervised learning
topic classification : supervised learning
https://www.machinelearningplus.com/nlp/topic-modeling-gensim-python/
https://www.baeldung.com/cs/ml-word2vec-topic-modeling
Topic Modeling: Predicting Multiple Tags of Research Articles using OneVsRest strategy: https://www.analyticsvidhya.com/blog/2021/09/onevsrest-classifier-for-predicting-multiple-tags-of-research-articles/#h2_2
Baeldung: Topic Modeling with Word2Vec https://www.baeldung.com/cs/ml-word2vec-topic-modeling
Evaluating Multi-label Classifiers https://towardsdatascience.com/evaluating-multi-label-classifiers-a31be83da6ea
Difference stemming lemmatization https://fr.quora.com/Quelle-est-la-diff%C3%A9rence-entre-le-stemming-et-la-lemmatisation